Own Every Moment at NetApp At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a principal-level product leader to own the AI product strategy for Azure NetApp Files (ANF)-a first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's "business builder" cloud roles, you will translate a fast-moving AI landscape into differentiated platform capabilities, joint roadmap bets with Microsoft, and enterprise outcomes (performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of enterprise storage, Azure AI infrastructure, and industry AI workloads, ensuring ANF is positioned and built as a strategic data foundation for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a highly strategic and deeply technical principal PM who can: Define multi-year AI vision and roadmap for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into concrete product requirements and joint go-to-market narratives with Microsoft. Balance hyperscaler co-development constraints with NetApp differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years product management in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing engineering and partner roadmaps without direct authority; experience with hyperscaler first-party or deeply partnered services is a strong plus. Excellent written and verbal communication to customers, executives, and engineers. Preferred Direct experience with Microsoft Azure AI services, GPU estates on Azure, and/or Azure Kubernetes Service + ML platform integrations. Familiarity with Databricks, Iceberg/Delta-class open table patterns, Kubernetes storage patterns, NVIDIA AI software stacks, and enterprise MLOps release cadences. Background in regulated industries and enterprise security/governance requirements for AI data. Education MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details
06/08/2026
Full time
Own Every Moment at NetApp At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a principal-level product leader to own the AI product strategy for Azure NetApp Files (ANF)-a first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's "business builder" cloud roles, you will translate a fast-moving AI landscape into differentiated platform capabilities, joint roadmap bets with Microsoft, and enterprise outcomes (performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of enterprise storage, Azure AI infrastructure, and industry AI workloads, ensuring ANF is positioned and built as a strategic data foundation for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a highly strategic and deeply technical principal PM who can: Define multi-year AI vision and roadmap for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into concrete product requirements and joint go-to-market narratives with Microsoft. Balance hyperscaler co-development constraints with NetApp differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years product management in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing engineering and partner roadmaps without direct authority; experience with hyperscaler first-party or deeply partnered services is a strong plus. Excellent written and verbal communication to customers, executives, and engineers. Preferred Direct experience with Microsoft Azure AI services, GPU estates on Azure, and/or Azure Kubernetes Service + ML platform integrations. Familiarity with Databricks, Iceberg/Delta-class open table patterns, Kubernetes storage patterns, NVIDIA AI software stacks, and enterprise MLOps release cadences. Background in regulated industries and enterprise security/governance requirements for AI data. Education MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details
Job Family: Data Engineering & Architecture Consulting Travel Required: Up to 25% Clearance Required: Active Secret What You Will Do As a Data Architect, you will lead the design, implementation, and evolution of enterprise scale data architectures supporting advanced analytics, AI/ML, and mission critical decision making for federal clients. You will serve as a technical authority responsible for ensuring data solutions are scalable, secure, and aligned with federal and DoD architecture, governance, and compliance requirements. You will work closely with data engineers, AI/ML teams, platform engineers, and government stakeholders to translate mission needs into robust data architectures, leveraging Databricks and Palantir Foundry as core delivery platforms. Key responsibilities include: Lead the end to end design of data architectures supporting analytics, AI/ML, and operational reporting Define logical and physical data models, ingestion patterns, transformation layers, and consumption frameworks Architect solutions for large scale structured and semi structured datasets, including federal financial and operational data Design secure, scalable data platforms leveraging Databricks (Spark, Delta Lake) and Palantir Foundry Establish architecture standards for data integration, metadata, lineage, and data quality Ensure architectures align with federal security, compliance, and data governance requirements Partner with AI/ML, MLOps, and analytics teams to enable production grade model development and deployment Guide implementation teams through complex architectural decisions and trade offs Support system design reviews, technical documentation, and government architecture forums Mentor junior architects and engineers while providing technical leadership across engagements What You Will Need: An ACTIVE and MAINTAINED "SECRET" Federal or DoD security clearance. Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field FIVE (5) or more years of experience in data architecture, data engineering, or enterprise analytics roles Hands on experience with Databricks (Spark, Delta Lake, performance optimization, production architectures) Hands on experience with Palantir Foundry, including data pipelines, ontology, and analytics enablement Strong experience designing enterprise data architectures supporting analytics and AI/ML workloads Experience with SQL and distributed data processing frameworks Experience integrating multiple data sources and managing complex data pipelines What Would Be Great to Have Strong understanding of data governance, metadata management, and lineage Ability to clearly communicate architectural designs to both technical and executive audiences Experience supporting the Department of Defense, including exposure to Advana or DoD enterprise data platforms Experience working with federal financial, budgetary, accounting, or audit data Experience architecting solutions in Azure Government or AWS GovCloud Familiarity with MLOps and how data architecture supports model lifecycle management Knowledge of data cataloging, semantic layers, and enterprise metadata strategies Experience designing data architectures that support Responsible AI and model governance Experience leading architecture reviews or serving as an approving authority Master's degree in a related technical discipline What We Offer: Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace. Benefits include: Medical, Rx, Dental & Vision Insurance Personal and Family Sick Time & Company Paid Holidays Position may be eligible for a discretionary variable incentive bonus Parental Leave and Adoption Assistance 401(k) Retirement Plan Basic Life & Supplemental Life Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts Short-Term & Long-Term Disability Student Loan PayDown Tuition Reimbursement, Personal Development & Learning Opportunities Skills Development & Certifications Employee Referral Program Corporate Sponsored Events & Community Outreach Emergency Back-Up Childcare Program Mobility Stipend About Guidehouse Guidehouse is an Equal Opportunity Employer-Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation. Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco. If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation. All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains or . Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process. If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse's Ethics Hotline. If you want to check the validity of correspondence you have received, please contact . Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant's dealings with unauthorized third parties. Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.
06/08/2026
Full time
Job Family: Data Engineering & Architecture Consulting Travel Required: Up to 25% Clearance Required: Active Secret What You Will Do As a Data Architect, you will lead the design, implementation, and evolution of enterprise scale data architectures supporting advanced analytics, AI/ML, and mission critical decision making for federal clients. You will serve as a technical authority responsible for ensuring data solutions are scalable, secure, and aligned with federal and DoD architecture, governance, and compliance requirements. You will work closely with data engineers, AI/ML teams, platform engineers, and government stakeholders to translate mission needs into robust data architectures, leveraging Databricks and Palantir Foundry as core delivery platforms. Key responsibilities include: Lead the end to end design of data architectures supporting analytics, AI/ML, and operational reporting Define logical and physical data models, ingestion patterns, transformation layers, and consumption frameworks Architect solutions for large scale structured and semi structured datasets, including federal financial and operational data Design secure, scalable data platforms leveraging Databricks (Spark, Delta Lake) and Palantir Foundry Establish architecture standards for data integration, metadata, lineage, and data quality Ensure architectures align with federal security, compliance, and data governance requirements Partner with AI/ML, MLOps, and analytics teams to enable production grade model development and deployment Guide implementation teams through complex architectural decisions and trade offs Support system design reviews, technical documentation, and government architecture forums Mentor junior architects and engineers while providing technical leadership across engagements What You Will Need: An ACTIVE and MAINTAINED "SECRET" Federal or DoD security clearance. Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field FIVE (5) or more years of experience in data architecture, data engineering, or enterprise analytics roles Hands on experience with Databricks (Spark, Delta Lake, performance optimization, production architectures) Hands on experience with Palantir Foundry, including data pipelines, ontology, and analytics enablement Strong experience designing enterprise data architectures supporting analytics and AI/ML workloads Experience with SQL and distributed data processing frameworks Experience integrating multiple data sources and managing complex data pipelines What Would Be Great to Have Strong understanding of data governance, metadata management, and lineage Ability to clearly communicate architectural designs to both technical and executive audiences Experience supporting the Department of Defense, including exposure to Advana or DoD enterprise data platforms Experience working with federal financial, budgetary, accounting, or audit data Experience architecting solutions in Azure Government or AWS GovCloud Familiarity with MLOps and how data architecture supports model lifecycle management Knowledge of data cataloging, semantic layers, and enterprise metadata strategies Experience designing data architectures that support Responsible AI and model governance Experience leading architecture reviews or serving as an approving authority Master's degree in a related technical discipline What We Offer: Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace. Benefits include: Medical, Rx, Dental & Vision Insurance Personal and Family Sick Time & Company Paid Holidays Position may be eligible for a discretionary variable incentive bonus Parental Leave and Adoption Assistance 401(k) Retirement Plan Basic Life & Supplemental Life Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts Short-Term & Long-Term Disability Student Loan PayDown Tuition Reimbursement, Personal Development & Learning Opportunities Skills Development & Certifications Employee Referral Program Corporate Sponsored Events & Community Outreach Emergency Back-Up Childcare Program Mobility Stipend About Guidehouse Guidehouse is an Equal Opportunity Employer-Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation. Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco. If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation. All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains or . Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process. If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse's Ethics Hotline. If you want to check the validity of correspondence you have received, please contact . Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant's dealings with unauthorized third parties. Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.
Job Family: Data Engineering & Architecture Consulting Travel Required: Up to 25% Clearance Required: Active Secret What You Will Do As a Data Architect, you will lead the design, implementation, and evolution of enterprise scale data architectures supporting advanced analytics, AI/ML, and mission critical decision making for federal clients. You will serve as a technical authority responsible for ensuring data solutions are scalable, secure, and aligned with federal and DoD architecture, governance, and compliance requirements. You will work closely with data engineers, AI/ML teams, platform engineers, and government stakeholders to translate mission needs into robust data architectures, leveraging Databricks and Palantir Foundry as core delivery platforms. Key responsibilities include: Lead the end to end design of data architectures supporting analytics, AI/ML, and operational reporting Define logical and physical data models, ingestion patterns, transformation layers, and consumption frameworks Architect solutions for large scale structured and semi structured datasets, including federal financial and operational data Design secure, scalable data platforms leveraging Databricks (Spark, Delta Lake) and Palantir Foundry Establish architecture standards for data integration, metadata, lineage, and data quality Ensure architectures align with federal security, compliance, and data governance requirements Partner with AI/ML, MLOps, and analytics teams to enable production grade model development and deployment Guide implementation teams through complex architectural decisions and trade offs Support system design reviews, technical documentation, and government architecture forums Mentor junior architects and engineers while providing technical leadership across engagements What You Will Need: An ACTIVE and MAINTAINED "SECRET" Federal or DoD security clearance. Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field FIVE (5) or more years of experience in data architecture, data engineering, or enterprise analytics roles Hands on experience with Databricks (Spark, Delta Lake, performance optimization, production architectures) Hands on experience with Palantir Foundry, including data pipelines, ontology, and analytics enablement Strong experience designing enterprise data architectures supporting analytics and AI/ML workloads Experience with SQL and distributed data processing frameworks Experience integrating multiple data sources and managing complex data pipelines What Would Be Great to Have Strong understanding of data governance, metadata management, and lineage Ability to clearly communicate architectural designs to both technical and executive audiences Experience supporting the Department of Defense, including exposure to Advana or DoD enterprise data platforms Experience working with federal financial, budgetary, accounting, or audit data Experience architecting solutions in Azure Government or AWS GovCloud Familiarity with MLOps and how data architecture supports model lifecycle management Knowledge of data cataloging, semantic layers, and enterprise metadata strategies Experience designing data architectures that support Responsible AI and model governance Experience leading architecture reviews or serving as an approving authority Master's degree in a related technical discipline What We Offer: Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace. Benefits include: Medical, Rx, Dental & Vision Insurance Personal and Family Sick Time & Company Paid Holidays Position may be eligible for a discretionary variable incentive bonus Parental Leave and Adoption Assistance 401(k) Retirement Plan Basic Life & Supplemental Life Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts Short-Term & Long-Term Disability Student Loan PayDown Tuition Reimbursement, Personal Development & Learning Opportunities Skills Development & Certifications Employee Referral Program Corporate Sponsored Events & Community Outreach Emergency Back-Up Childcare Program Mobility Stipend About Guidehouse Guidehouse is an Equal Opportunity Employer-Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation. Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco. If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation. All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains or . Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process. If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse's Ethics Hotline. If you want to check the validity of correspondence you have received, please contact . Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant's dealings with unauthorized third parties. Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.
06/08/2026
Full time
Job Family: Data Engineering & Architecture Consulting Travel Required: Up to 25% Clearance Required: Active Secret What You Will Do As a Data Architect, you will lead the design, implementation, and evolution of enterprise scale data architectures supporting advanced analytics, AI/ML, and mission critical decision making for federal clients. You will serve as a technical authority responsible for ensuring data solutions are scalable, secure, and aligned with federal and DoD architecture, governance, and compliance requirements. You will work closely with data engineers, AI/ML teams, platform engineers, and government stakeholders to translate mission needs into robust data architectures, leveraging Databricks and Palantir Foundry as core delivery platforms. Key responsibilities include: Lead the end to end design of data architectures supporting analytics, AI/ML, and operational reporting Define logical and physical data models, ingestion patterns, transformation layers, and consumption frameworks Architect solutions for large scale structured and semi structured datasets, including federal financial and operational data Design secure, scalable data platforms leveraging Databricks (Spark, Delta Lake) and Palantir Foundry Establish architecture standards for data integration, metadata, lineage, and data quality Ensure architectures align with federal security, compliance, and data governance requirements Partner with AI/ML, MLOps, and analytics teams to enable production grade model development and deployment Guide implementation teams through complex architectural decisions and trade offs Support system design reviews, technical documentation, and government architecture forums Mentor junior architects and engineers while providing technical leadership across engagements What You Will Need: An ACTIVE and MAINTAINED "SECRET" Federal or DoD security clearance. Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field FIVE (5) or more years of experience in data architecture, data engineering, or enterprise analytics roles Hands on experience with Databricks (Spark, Delta Lake, performance optimization, production architectures) Hands on experience with Palantir Foundry, including data pipelines, ontology, and analytics enablement Strong experience designing enterprise data architectures supporting analytics and AI/ML workloads Experience with SQL and distributed data processing frameworks Experience integrating multiple data sources and managing complex data pipelines What Would Be Great to Have Strong understanding of data governance, metadata management, and lineage Ability to clearly communicate architectural designs to both technical and executive audiences Experience supporting the Department of Defense, including exposure to Advana or DoD enterprise data platforms Experience working with federal financial, budgetary, accounting, or audit data Experience architecting solutions in Azure Government or AWS GovCloud Familiarity with MLOps and how data architecture supports model lifecycle management Knowledge of data cataloging, semantic layers, and enterprise metadata strategies Experience designing data architectures that support Responsible AI and model governance Experience leading architecture reviews or serving as an approving authority Master's degree in a related technical discipline What We Offer: Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace. Benefits include: Medical, Rx, Dental & Vision Insurance Personal and Family Sick Time & Company Paid Holidays Position may be eligible for a discretionary variable incentive bonus Parental Leave and Adoption Assistance 401(k) Retirement Plan Basic Life & Supplemental Life Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts Short-Term & Long-Term Disability Student Loan PayDown Tuition Reimbursement, Personal Development & Learning Opportunities Skills Development & Certifications Employee Referral Program Corporate Sponsored Events & Community Outreach Emergency Back-Up Childcare Program Mobility Stipend About Guidehouse Guidehouse is an Equal Opportunity Employer-Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation. Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco. If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation. All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains or . Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process. If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse's Ethics Hotline. If you want to check the validity of correspondence you have received, please contact . Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant's dealings with unauthorized third parties. Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.
Job Family: Data Engineering & Architecture Consulting Travel Required: Up to 25% Clearance Required: Active Secret What You Will Do As a Data Architect, you will lead the design, implementation, and evolution of enterprise scale data architectures supporting advanced analytics, AI/ML, and mission critical decision making for federal clients. You will serve as a technical authority responsible for ensuring data solutions are scalable, secure, and aligned with federal and DoD architecture, governance, and compliance requirements. You will work closely with data engineers, AI/ML teams, platform engineers, and government stakeholders to translate mission needs into robust data architectures, leveraging Databricks and Palantir Foundry as core delivery platforms. Key responsibilities include: Lead the end to end design of data architectures supporting analytics, AI/ML, and operational reporting Define logical and physical data models, ingestion patterns, transformation layers, and consumption frameworks Architect solutions for large scale structured and semi structured datasets, including federal financial and operational data Design secure, scalable data platforms leveraging Databricks (Spark, Delta Lake) and Palantir Foundry Establish architecture standards for data integration, metadata, lineage, and data quality Ensure architectures align with federal security, compliance, and data governance requirements Partner with AI/ML, MLOps, and analytics teams to enable production grade model development and deployment Guide implementation teams through complex architectural decisions and trade offs Support system design reviews, technical documentation, and government architecture forums Mentor junior architects and engineers while providing technical leadership across engagements What You Will Need: An ACTIVE and MAINTAINED "SECRET" Federal or DoD security clearance. Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field FIVE (5) or more years of experience in data architecture, data engineering, or enterprise analytics roles Hands on experience with Databricks (Spark, Delta Lake, performance optimization, production architectures) Hands on experience with Palantir Foundry, including data pipelines, ontology, and analytics enablement Strong experience designing enterprise data architectures supporting analytics and AI/ML workloads Experience with SQL and distributed data processing frameworks Experience integrating multiple data sources and managing complex data pipelines What Would Be Great to Have Strong understanding of data governance, metadata management, and lineage Ability to clearly communicate architectural designs to both technical and executive audiences Experience supporting the Department of Defense, including exposure to Advana or DoD enterprise data platforms Experience working with federal financial, budgetary, accounting, or audit data Experience architecting solutions in Azure Government or AWS GovCloud Familiarity with MLOps and how data architecture supports model lifecycle management Knowledge of data cataloging, semantic layers, and enterprise metadata strategies Experience designing data architectures that support Responsible AI and model governance Experience leading architecture reviews or serving as an approving authority Master's degree in a related technical discipline What We Offer: Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace. Benefits include: Medical, Rx, Dental & Vision Insurance Personal and Family Sick Time & Company Paid Holidays Position may be eligible for a discretionary variable incentive bonus Parental Leave and Adoption Assistance 401(k) Retirement Plan Basic Life & Supplemental Life Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts Short-Term & Long-Term Disability Student Loan PayDown Tuition Reimbursement, Personal Development & Learning Opportunities Skills Development & Certifications Employee Referral Program Corporate Sponsored Events & Community Outreach Emergency Back-Up Childcare Program Mobility Stipend About Guidehouse Guidehouse is an Equal Opportunity Employer-Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation. Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco. If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation. All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains or . Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process. If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse's Ethics Hotline. If you want to check the validity of correspondence you have received, please contact . Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant's dealings with unauthorized third parties. Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.
06/08/2026
Full time
Job Family: Data Engineering & Architecture Consulting Travel Required: Up to 25% Clearance Required: Active Secret What You Will Do As a Data Architect, you will lead the design, implementation, and evolution of enterprise scale data architectures supporting advanced analytics, AI/ML, and mission critical decision making for federal clients. You will serve as a technical authority responsible for ensuring data solutions are scalable, secure, and aligned with federal and DoD architecture, governance, and compliance requirements. You will work closely with data engineers, AI/ML teams, platform engineers, and government stakeholders to translate mission needs into robust data architectures, leveraging Databricks and Palantir Foundry as core delivery platforms. Key responsibilities include: Lead the end to end design of data architectures supporting analytics, AI/ML, and operational reporting Define logical and physical data models, ingestion patterns, transformation layers, and consumption frameworks Architect solutions for large scale structured and semi structured datasets, including federal financial and operational data Design secure, scalable data platforms leveraging Databricks (Spark, Delta Lake) and Palantir Foundry Establish architecture standards for data integration, metadata, lineage, and data quality Ensure architectures align with federal security, compliance, and data governance requirements Partner with AI/ML, MLOps, and analytics teams to enable production grade model development and deployment Guide implementation teams through complex architectural decisions and trade offs Support system design reviews, technical documentation, and government architecture forums Mentor junior architects and engineers while providing technical leadership across engagements What You Will Need: An ACTIVE and MAINTAINED "SECRET" Federal or DoD security clearance. Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field FIVE (5) or more years of experience in data architecture, data engineering, or enterprise analytics roles Hands on experience with Databricks (Spark, Delta Lake, performance optimization, production architectures) Hands on experience with Palantir Foundry, including data pipelines, ontology, and analytics enablement Strong experience designing enterprise data architectures supporting analytics and AI/ML workloads Experience with SQL and distributed data processing frameworks Experience integrating multiple data sources and managing complex data pipelines What Would Be Great to Have Strong understanding of data governance, metadata management, and lineage Ability to clearly communicate architectural designs to both technical and executive audiences Experience supporting the Department of Defense, including exposure to Advana or DoD enterprise data platforms Experience working with federal financial, budgetary, accounting, or audit data Experience architecting solutions in Azure Government or AWS GovCloud Familiarity with MLOps and how data architecture supports model lifecycle management Knowledge of data cataloging, semantic layers, and enterprise metadata strategies Experience designing data architectures that support Responsible AI and model governance Experience leading architecture reviews or serving as an approving authority Master's degree in a related technical discipline What We Offer: Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace. Benefits include: Medical, Rx, Dental & Vision Insurance Personal and Family Sick Time & Company Paid Holidays Position may be eligible for a discretionary variable incentive bonus Parental Leave and Adoption Assistance 401(k) Retirement Plan Basic Life & Supplemental Life Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts Short-Term & Long-Term Disability Student Loan PayDown Tuition Reimbursement, Personal Development & Learning Opportunities Skills Development & Certifications Employee Referral Program Corporate Sponsored Events & Community Outreach Emergency Back-Up Childcare Program Mobility Stipend About Guidehouse Guidehouse is an Equal Opportunity Employer-Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation. Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco. If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation. All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains or . Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process. If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse's Ethics Hotline. If you want to check the validity of correspondence you have received, please contact . Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant's dealings with unauthorized third parties. Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.
Own Every Moment at NetApp At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a principal-level product leader to own the AI product strategy for Azure NetApp Files (ANF)-a first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's "business builder" cloud roles, you will translate a fast-moving AI landscape into differentiated platform capabilities, joint roadmap bets with Microsoft, and enterprise outcomes (performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of enterprise storage, Azure AI infrastructure, and industry AI workloads, ensuring ANF is positioned and built as a strategic data foundation for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a highly strategic and deeply technical principal PM who can: Define multi-year AI vision and roadmap for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into concrete product requirements and joint go-to-market narratives with Microsoft. Balance hyperscaler co-development constraints with NetApp differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years product management in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing engineering and partner roadmaps without direct authority; experience with hyperscaler first-party or deeply partnered services is a strong plus. Excellent written and verbal communication to customers, executives, and engineers. Preferred Direct experience with Microsoft Azure AI services, GPU estates on Azure, and/or Azure Kubernetes Service + ML platform integrations. Familiarity with Databricks, Iceberg/Delta-class open table patterns, Kubernetes storage patterns, NVIDIA AI software stacks, and enterprise MLOps release cadences. Background in regulated industries and enterprise security/governance requirements for AI data. Education MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details
06/08/2026
Full time
Own Every Moment at NetApp At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a principal-level product leader to own the AI product strategy for Azure NetApp Files (ANF)-a first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's "business builder" cloud roles, you will translate a fast-moving AI landscape into differentiated platform capabilities, joint roadmap bets with Microsoft, and enterprise outcomes (performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of enterprise storage, Azure AI infrastructure, and industry AI workloads, ensuring ANF is positioned and built as a strategic data foundation for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a highly strategic and deeply technical principal PM who can: Define multi-year AI vision and roadmap for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into concrete product requirements and joint go-to-market narratives with Microsoft. Balance hyperscaler co-development constraints with NetApp differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years product management in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing engineering and partner roadmaps without direct authority; experience with hyperscaler first-party or deeply partnered services is a strong plus. Excellent written and verbal communication to customers, executives, and engineers. Preferred Direct experience with Microsoft Azure AI services, GPU estates on Azure, and/or Azure Kubernetes Service + ML platform integrations. Familiarity with Databricks, Iceberg/Delta-class open table patterns, Kubernetes storage patterns, NVIDIA AI software stacks, and enterprise MLOps release cadences. Background in regulated industries and enterprise security/governance requirements for AI data. Education MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details
Disney Entertainment Television Careers
New York, New York
This is not a remote role. You must be in the local area or willing to relocate. Department/Group Overview: The cross-media measurement and advanced analytics organization is responsible for data strategy & management, cross-platform content measurement, Content marketing measurement, and linear and digital inventory forecasting. The team provides advanced analytics and actionable insights related to Disney entertainment's content, monetization, and audience development. The Data and Analytics Operations team is part of the Cross-Media Measurement and Advanced Analytics organization (CMMAA). Reporting to the Executive Director of Data and Analytics Operations, this team leverages advanced machine learning techniques to deliver a robust suite of analytics solutions. Their portfolio includes descriptive, predictive, and prescriptive analytics, underpinned by strong data management practices and an interoperability layer. These capabilities are structured to support a range of business goals, such as content production, marketing and monetization. Job Summary: The Lead Machine Learning Engineer is a senior individual contributor who provides technical leadership for complex machine learning systems and the data foundations required to operate them. This role applies machine learning techniques in code (e.g., supervised/unsupervised learning, deep learning/neural networks where appropriate, and advanced modeling approaches) to build predictive systems at scale for identity, audience, and cross-platform measurement. The position also leads architecture and standards for ML pipelines that capture, manage, store, and utilize large-scale structured and unstructured data, ensuring data integrity, interoperability, and reliability across production environments. Responsibilities and Duties of the Role: • Lead development, training, and deployment of advanced ML models for identity resolution, look-alike modeling, and cross-platform measurement; translate algorithms into production-quality code; optimize for scale and performance. • Architect scalable ML platforms and reusable components (training/inference pipelines, feature/label foundations, model serving patterns) that operate across distributed cloud and platform environments • Lead data and feature foundations: define data contracts, metadata/lineage expectations, and automated quality controls to maintain data integrity across structured/unstructured sources in Snowflake/Databricks. • MLOps & reliability: establish CI/CD patterns, model versioning/registry practices, automated evaluation, drift detection, monitoring dashboards/alerts, and operational playbooks for sustained production health. • Cross-functional technical leadership: drive design reviews, clarify technical requirements, and lead multi-quarter initiatives with product, analytics, and platform engineering stakeholders. • Mentorship & enablement: mentor engineers through code/design reviews; build shared libraries and best practices to improve team velocity and quality. • Privacy, governance & compliance: ensure privacy-by-design practices, PII safeguards, documentation, and audit readiness across ML workflows (GDPR/CCPA). Required Education, Experience/Skills/Training: Minimum Qualifications: • Must have strong production experience with deep-learning, genAI, or retrieval-augmented systems (PyTorch, vector databases) and real-time data pipelines (Kafka, Pub/Sub, Kinesis) • Must have 7+ years of professional experience delivering production ML systems (models + pipelines + monitoring) at scale • Must have advanced coding skills in Python and SQL; strong software engineering discipline (testing, CI/CD, code review, design documentation) • Must have demonstrated experience applying ML techniques in code to develop predictive systems at scale (including deep learning where appropriate) • Must have hands-on expertise with cloud-native data platforms and distributed compute (Snowflake/Databricks/Spark/BigQuery) and container orchestration (Docker/Kubernetes) • Proven ability to lead technical initiatives across teams and influence architecture and standards Preferred Qualifications: • 8+ years total experience, with hands-on work in media, advertising technology, or cross-platform audience measurement • Strong understanding with modern MLOps stacks (e.g., MLflow, Kubeflow, Vertex AI, SageMaker) and model-governance practices (metadata, lineage, drift detection) • Certifications such as Google Professional Machine Learning Engineer, AWS Certified Machine Learning - Specialty, or equivalent cloud/data credentials • Contributions to open-source ML or data-engineering projects, conference presentations, or peer-reviewed publications • Experience in media/ad tech, identity graphs, audience measurement, or interoperability layers • Experience with modern MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker) and model governance practices Required Education: • Bachelor's degree in a relevant technical or science field (e.g. computer science, data science, mathematics, or a related discipline) Preferred Education: • Master's degree or PhD in a relevant field (e.g., Applied Math, Computer Science, Computational Science, Operation Research, Data Science) The hiring range for this position in New York City is $179,700.00 - $225,000.00 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
06/08/2026
Full time
This is not a remote role. You must be in the local area or willing to relocate. Department/Group Overview: The cross-media measurement and advanced analytics organization is responsible for data strategy & management, cross-platform content measurement, Content marketing measurement, and linear and digital inventory forecasting. The team provides advanced analytics and actionable insights related to Disney entertainment's content, monetization, and audience development. The Data and Analytics Operations team is part of the Cross-Media Measurement and Advanced Analytics organization (CMMAA). Reporting to the Executive Director of Data and Analytics Operations, this team leverages advanced machine learning techniques to deliver a robust suite of analytics solutions. Their portfolio includes descriptive, predictive, and prescriptive analytics, underpinned by strong data management practices and an interoperability layer. These capabilities are structured to support a range of business goals, such as content production, marketing and monetization. Job Summary: The Lead Machine Learning Engineer is a senior individual contributor who provides technical leadership for complex machine learning systems and the data foundations required to operate them. This role applies machine learning techniques in code (e.g., supervised/unsupervised learning, deep learning/neural networks where appropriate, and advanced modeling approaches) to build predictive systems at scale for identity, audience, and cross-platform measurement. The position also leads architecture and standards for ML pipelines that capture, manage, store, and utilize large-scale structured and unstructured data, ensuring data integrity, interoperability, and reliability across production environments. Responsibilities and Duties of the Role: • Lead development, training, and deployment of advanced ML models for identity resolution, look-alike modeling, and cross-platform measurement; translate algorithms into production-quality code; optimize for scale and performance. • Architect scalable ML platforms and reusable components (training/inference pipelines, feature/label foundations, model serving patterns) that operate across distributed cloud and platform environments • Lead data and feature foundations: define data contracts, metadata/lineage expectations, and automated quality controls to maintain data integrity across structured/unstructured sources in Snowflake/Databricks. • MLOps & reliability: establish CI/CD patterns, model versioning/registry practices, automated evaluation, drift detection, monitoring dashboards/alerts, and operational playbooks for sustained production health. • Cross-functional technical leadership: drive design reviews, clarify technical requirements, and lead multi-quarter initiatives with product, analytics, and platform engineering stakeholders. • Mentorship & enablement: mentor engineers through code/design reviews; build shared libraries and best practices to improve team velocity and quality. • Privacy, governance & compliance: ensure privacy-by-design practices, PII safeguards, documentation, and audit readiness across ML workflows (GDPR/CCPA). Required Education, Experience/Skills/Training: Minimum Qualifications: • Must have strong production experience with deep-learning, genAI, or retrieval-augmented systems (PyTorch, vector databases) and real-time data pipelines (Kafka, Pub/Sub, Kinesis) • Must have 7+ years of professional experience delivering production ML systems (models + pipelines + monitoring) at scale • Must have advanced coding skills in Python and SQL; strong software engineering discipline (testing, CI/CD, code review, design documentation) • Must have demonstrated experience applying ML techniques in code to develop predictive systems at scale (including deep learning where appropriate) • Must have hands-on expertise with cloud-native data platforms and distributed compute (Snowflake/Databricks/Spark/BigQuery) and container orchestration (Docker/Kubernetes) • Proven ability to lead technical initiatives across teams and influence architecture and standards Preferred Qualifications: • 8+ years total experience, with hands-on work in media, advertising technology, or cross-platform audience measurement • Strong understanding with modern MLOps stacks (e.g., MLflow, Kubeflow, Vertex AI, SageMaker) and model-governance practices (metadata, lineage, drift detection) • Certifications such as Google Professional Machine Learning Engineer, AWS Certified Machine Learning - Specialty, or equivalent cloud/data credentials • Contributions to open-source ML or data-engineering projects, conference presentations, or peer-reviewed publications • Experience in media/ad tech, identity graphs, audience measurement, or interoperability layers • Experience with modern MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker) and model governance practices Required Education: • Bachelor's degree in a relevant technical or science field (e.g. computer science, data science, mathematics, or a related discipline) Preferred Education: • Master's degree or PhD in a relevant field (e.g., Applied Math, Computer Science, Computational Science, Operation Research, Data Science) The hiring range for this position in New York City is $179,700.00 - $225,000.00 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
Own Every Moment at NetApp At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a principal-level product leader to own the AI product strategy for Azure NetApp Files (ANF)-a first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's "business builder" cloud roles, you will translate a fast-moving AI landscape into differentiated platform capabilities, joint roadmap bets with Microsoft, and enterprise outcomes (performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of enterprise storage, Azure AI infrastructure, and industry AI workloads, ensuring ANF is positioned and built as a strategic data foundation for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a highly strategic and deeply technical principal PM who can: Define multi-year AI vision and roadmap for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into concrete product requirements and joint go-to-market narratives with Microsoft. Balance hyperscaler co-development constraints with NetApp differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years product management in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing engineering and partner roadmaps without direct authority; experience with hyperscaler first-party or deeply partnered services is a strong plus. Excellent written and verbal communication to customers, executives, and engineers. Preferred Direct experience with Microsoft Azure AI services, GPU estates on Azure, and/or Azure Kubernetes Service + ML platform integrations. Familiarity with Databricks, Iceberg/Delta-class open table patterns, Kubernetes storage patterns, NVIDIA AI software stacks, and enterprise MLOps release cadences. Background in regulated industries and enterprise security/governance requirements for AI data. Education MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details
06/08/2026
Full time
Own Every Moment at NetApp At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a principal-level product leader to own the AI product strategy for Azure NetApp Files (ANF)-a first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's "business builder" cloud roles, you will translate a fast-moving AI landscape into differentiated platform capabilities, joint roadmap bets with Microsoft, and enterprise outcomes (performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of enterprise storage, Azure AI infrastructure, and industry AI workloads, ensuring ANF is positioned and built as a strategic data foundation for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a highly strategic and deeply technical principal PM who can: Define multi-year AI vision and roadmap for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into concrete product requirements and joint go-to-market narratives with Microsoft. Balance hyperscaler co-development constraints with NetApp differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years product management in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing engineering and partner roadmaps without direct authority; experience with hyperscaler first-party or deeply partnered services is a strong plus. Excellent written and verbal communication to customers, executives, and engineers. Preferred Direct experience with Microsoft Azure AI services, GPU estates on Azure, and/or Azure Kubernetes Service + ML platform integrations. Familiarity with Databricks, Iceberg/Delta-class open table patterns, Kubernetes storage patterns, NVIDIA AI software stacks, and enterprise MLOps release cadences. Background in regulated industries and enterprise security/governance requirements for AI data. Education MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details
Own Every Moment at NetApp While professional experience and qualifications are key for this role, make sure to check you have the preferable soft skills before applying if required. At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a?principal-level product leader?to own the?AI product strategy?for?Azure NetApp Files (ANF)-a?first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's?"business builder"?cloud roles, you will translate a fast-moving AI landscape into?differentiated platform capabilities,?joint roadmap bets?with Microsoft, and?enterprise outcomes?(performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of?enterprise storage,?Azure AI infrastructure, and?industry AI workloads, ensuring ANF is positioned and built as a?strategic data foundation?for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a?highly strategic and deeply technical?principal PM who can: Define?multi-year AI vision and roadmap?for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into?concrete product requirements?and?joint go-to-market?narratives with Microsoft. Balance?hyperscaler co-development?constraints with?NetApp differentiation?(enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end?AI strategy?for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across?performance,?scale,?data services,?protocol and API surfaces, and?operational excellence?for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with?Microsoft?teams across?Azure AI / Foundry,?Azure Machine Learning,?AKS / container platforms,?GPU infrastructure,?data/analytics?(e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with?Azure-wide AI data?guidance and reference architectures, and feed?real customer workload evidence?back into joint planning. Cross-functional leadership Lead across?engineering, product marketing, sales, customer success, and professional services?to ship capabilities and repeatable?reference architectures / proof points. Engage?strategic customers?and?design partners?to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into?differentiated bets. Represent ANF as a?credible technical executive?in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example:?semiconductor/EDA,?manufacturing,?healthcare imaging,?financial services,?energy,?media & entertainment, and?HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end?AI strategy?for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across?performance,?scale,?data services,?protocol and API surfaces, and?operational excellence?for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with?Microsoft?teams across?Azure AI / Foundry,?Azure Machine Learning,?AKS / container platforms,?GPU infrastructure,?data/analytics?(e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with?Azure-wide AI data?guidance and reference architectures, and feed?real customer workload evidence?back into joint planning. Cross-functional leadership Lead across?engineering, product marketing, sales, customer success, and professional services?to ship capabilities and repeatable?reference architectures / proof points. Engage?strategic customers?and?design partners?to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into?differentiated bets. Represent ANF as a?credible technical executive?in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example:?semiconductor/EDA,?manufacturing,?healthcare imaging,?financial services,?energy,?media & entertainment, and?HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years?product management in?cloud infrastructure,?enterprise storage,?AI/ML infrastructure, or?data platforms?(principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of?enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with?modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing?engineering and partner roadmaps?without direct authority; experience with?hyperscaler first-party?or deeply partnered services is a strong plus. Excellent written and verbal communication to?customers, executives, and engineers. Preferred Direct experience with?Microsoft Azure?AI services,?GPU?estates on Azure, and/or?Azure Kubernetes Service?+ ML platform integrations. Familiarity with?Databricks,?Iceberg/Delta-class open table patterns,?Kubernetes?storage patterns,?NVIDIA AI?software stacks, and?enterprise MLOps?release cadences. Background in?regulated industries?and enterprise security/governance requirements for AI data. Education MBA?or advanced degree in?CS/Engineering?(helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: . click apply for full job details
06/08/2026
Full time
Own Every Moment at NetApp While professional experience and qualifications are key for this role, make sure to check you have the preferable soft skills before applying if required. At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a?principal-level product leader?to own the?AI product strategy?for?Azure NetApp Files (ANF)-a?first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's?"business builder"?cloud roles, you will translate a fast-moving AI landscape into?differentiated platform capabilities,?joint roadmap bets?with Microsoft, and?enterprise outcomes?(performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of?enterprise storage,?Azure AI infrastructure, and?industry AI workloads, ensuring ANF is positioned and built as a?strategic data foundation?for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a?highly strategic and deeply technical?principal PM who can: Define?multi-year AI vision and roadmap?for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into?concrete product requirements?and?joint go-to-market?narratives with Microsoft. Balance?hyperscaler co-development?constraints with?NetApp differentiation?(enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end?AI strategy?for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across?performance,?scale,?data services,?protocol and API surfaces, and?operational excellence?for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with?Microsoft?teams across?Azure AI / Foundry,?Azure Machine Learning,?AKS / container platforms,?GPU infrastructure,?data/analytics?(e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with?Azure-wide AI data?guidance and reference architectures, and feed?real customer workload evidence?back into joint planning. Cross-functional leadership Lead across?engineering, product marketing, sales, customer success, and professional services?to ship capabilities and repeatable?reference architectures / proof points. Engage?strategic customers?and?design partners?to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into?differentiated bets. Represent ANF as a?credible technical executive?in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example:?semiconductor/EDA,?manufacturing,?healthcare imaging,?financial services,?energy,?media & entertainment, and?HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end?AI strategy?for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across?performance,?scale,?data services,?protocol and API surfaces, and?operational excellence?for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with?Microsoft?teams across?Azure AI / Foundry,?Azure Machine Learning,?AKS / container platforms,?GPU infrastructure,?data/analytics?(e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with?Azure-wide AI data?guidance and reference architectures, and feed?real customer workload evidence?back into joint planning. Cross-functional leadership Lead across?engineering, product marketing, sales, customer success, and professional services?to ship capabilities and repeatable?reference architectures / proof points. Engage?strategic customers?and?design partners?to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into?differentiated bets. Represent ANF as a?credible technical executive?in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example:?semiconductor/EDA,?manufacturing,?healthcare imaging,?financial services,?energy,?media & entertainment, and?HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years?product management in?cloud infrastructure,?enterprise storage,?AI/ML infrastructure, or?data platforms?(principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of?enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with?modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing?engineering and partner roadmaps?without direct authority; experience with?hyperscaler first-party?or deeply partnered services is a strong plus. Excellent written and verbal communication to?customers, executives, and engineers. Preferred Direct experience with?Microsoft Azure?AI services,?GPU?estates on Azure, and/or?Azure Kubernetes Service?+ ML platform integrations. Familiarity with?Databricks,?Iceberg/Delta-class open table patterns,?Kubernetes?storage patterns,?NVIDIA AI?software stacks, and?enterprise MLOps?release cadences. Background in?regulated industries?and enterprise security/governance requirements for AI data. Education MBA?or advanced degree in?CS/Engineering?(helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: . click apply for full job details
Own Every Moment at NetApp At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a principal-level product leader to own the AI product strategy for Azure NetApp Files (ANF)-a first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's "business builder" cloud roles, you will translate a fast-moving AI landscape into differentiated platform capabilities, joint roadmap bets with Microsoft, and enterprise outcomes (performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of enterprise storage, Azure AI infrastructure, and industry AI workloads, ensuring ANF is positioned and built as a strategic data foundation for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a highly strategic and deeply technical principal PM who can: Define multi-year AI vision and roadmap for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into concrete product requirements and joint go-to-market narratives with Microsoft. Balance hyperscaler co-development constraints with NetApp differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years product management in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing engineering and partner roadmaps without direct authority; experience with hyperscaler first-party or deeply partnered services is a strong plus. Excellent written and verbal communication to customers, executives, and engineers. Preferred Direct experience with Microsoft Azure AI services, GPU estates on Azure, and/or Azure Kubernetes Service + ML platform integrations. Familiarity with Databricks, Iceberg/Delta-class open table patterns, Kubernetes storage patterns, NVIDIA AI software stacks, and enterprise MLOps release cadences. Background in regulated industries and enterprise security/governance requirements for AI data. Education MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details
06/08/2026
Full time
Own Every Moment at NetApp At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a principal-level product leader to own the AI product strategy for Azure NetApp Files (ANF)-a first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's "business builder" cloud roles, you will translate a fast-moving AI landscape into differentiated platform capabilities, joint roadmap bets with Microsoft, and enterprise outcomes (performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of enterprise storage, Azure AI infrastructure, and industry AI workloads, ensuring ANF is positioned and built as a strategic data foundation for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a highly strategic and deeply technical principal PM who can: Define multi-year AI vision and roadmap for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into concrete product requirements and joint go-to-market narratives with Microsoft. Balance hyperscaler co-development constraints with NetApp differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS / container platforms, GPU infrastructure, data/analytics (e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning. Cross-functional leadership Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points. Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into differentiated bets. Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years product management in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing engineering and partner roadmaps without direct authority; experience with hyperscaler first-party or deeply partnered services is a strong plus. Excellent written and verbal communication to customers, executives, and engineers. Preferred Direct experience with Microsoft Azure AI services, GPU estates on Azure, and/or Azure Kubernetes Service + ML platform integrations. Familiarity with Databricks, Iceberg/Delta-class open table patterns, Kubernetes storage patterns, NVIDIA AI software stacks, and enterprise MLOps release cadences. Background in regulated industries and enterprise security/governance requirements for AI data. Education MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details
Own Every Moment at NetApp While professional experience and qualifications are key for this role, make sure to check you have the preferable soft skills before applying if required. At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a?principal-level product leader?to own the?AI product strategy?for?Azure NetApp Files (ANF)-a?first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's?"business builder"?cloud roles, you will translate a fast-moving AI landscape into?differentiated platform capabilities,?joint roadmap bets?with Microsoft, and?enterprise outcomes?(performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of?enterprise storage,?Azure AI infrastructure, and?industry AI workloads, ensuring ANF is positioned and built as a?strategic data foundation?for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a?highly strategic and deeply technical?principal PM who can: Define?multi-year AI vision and roadmap?for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into?concrete product requirements?and?joint go-to-market?narratives with Microsoft. Balance?hyperscaler co-development?constraints with?NetApp differentiation?(enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end?AI strategy?for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across?performance,?scale,?data services,?protocol and API surfaces, and?operational excellence?for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with?Microsoft?teams across?Azure AI / Foundry,?Azure Machine Learning,?AKS / container platforms,?GPU infrastructure,?data/analytics?(e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with?Azure-wide AI data?guidance and reference architectures, and feed?real customer workload evidence?back into joint planning. Cross-functional leadership Lead across?engineering, product marketing, sales, customer success, and professional services?to ship capabilities and repeatable?reference architectures / proof points. Engage?strategic customers?and?design partners?to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into?differentiated bets. Represent ANF as a?credible technical executive?in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example:?semiconductor/EDA,?manufacturing,?healthcare imaging,?financial services,?energy,?media & entertainment, and?HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end?AI strategy?for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across?performance,?scale,?data services,?protocol and API surfaces, and?operational excellence?for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with?Microsoft?teams across?Azure AI / Foundry,?Azure Machine Learning,?AKS / container platforms,?GPU infrastructure,?data/analytics?(e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with?Azure-wide AI data?guidance and reference architectures, and feed?real customer workload evidence?back into joint planning. Cross-functional leadership Lead across?engineering, product marketing, sales, customer success, and professional services?to ship capabilities and repeatable?reference architectures / proof points. Engage?strategic customers?and?design partners?to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into?differentiated bets. Represent ANF as a?credible technical executive?in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example:?semiconductor/EDA,?manufacturing,?healthcare imaging,?financial services,?energy,?media & entertainment, and?HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years?product management in?cloud infrastructure,?enterprise storage,?AI/ML infrastructure, or?data platforms?(principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of?enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with?modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing?engineering and partner roadmaps?without direct authority; experience with?hyperscaler first-party?or deeply partnered services is a strong plus. Excellent written and verbal communication to?customers, executives, and engineers. Preferred Direct experience with?Microsoft Azure?AI services,?GPU?estates on Azure, and/or?Azure Kubernetes Service?+ ML platform integrations. Familiarity with?Databricks,?Iceberg/Delta-class open table patterns,?Kubernetes?storage patterns,?NVIDIA AI?software stacks, and?enterprise MLOps?release cadences. Background in?regulated industries?and enterprise security/governance requirements for AI data. Education MBA?or advanced degree in?CS/Engineering?(helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: . click apply for full job details
06/08/2026
Full time
Own Every Moment at NetApp While professional experience and qualifications are key for this role, make sure to check you have the preferable soft skills before applying if required. At NetApp, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Job Summary NetApp is hiring a?principal-level product leader?to own the?AI product strategy?for?Azure NetApp Files (ANF)-a?first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's?"business builder"?cloud roles, you will translate a fast-moving AI landscape into?differentiated platform capabilities,?joint roadmap bets?with Microsoft, and?enterprise outcomes?(performance, data locality, governance, and time-to-value for AI pipelines). You will sit at the intersection of?enterprise storage,?Azure AI infrastructure, and?industry AI workloads, ensuring ANF is positioned and built as a?strategic data foundation?for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. Role Overview We need a?highly strategic and deeply technical?principal PM who can: Define?multi-year AI vision and roadmap?for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into?concrete product requirements?and?joint go-to-market?narratives with Microsoft. Balance?hyperscaler co-development?constraints with?NetApp differentiation?(enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). Responsibilities AI strategy & roadmap Own end-to-end?AI strategy?for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across?performance,?scale,?data services,?protocol and API surfaces, and?operational excellence?for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with?Microsoft?teams across?Azure AI / Foundry,?Azure Machine Learning,?AKS / container platforms,?GPU infrastructure,?data/analytics?(e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with?Azure-wide AI data?guidance and reference architectures, and feed?real customer workload evidence?back into joint planning. Cross-functional leadership Lead across?engineering, product marketing, sales, customer success, and professional services?to ship capabilities and repeatable?reference architectures / proof points. Engage?strategic customers?and?design partners?to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into?differentiated bets. Represent ANF as a?credible technical executive?in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example:?semiconductor/EDA,?manufacturing,?healthcare imaging,?financial services,?energy,?media & entertainment, and?HPC/simulation-including compliance and data residency realities. AI strategy & roadmap Own end-to-end?AI strategy?for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. Prioritize investments across?performance,?scale,?data services,?protocol and API surfaces, and?operational excellence?for AI pipelines. Workload-led product definition Drive requirements for AI-centric scenarios, including: Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) RAG and enterprise search (datasets, versioning, clones, refresh patterns) Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) Large multimodal and enterprise datasets (governance, access control, lifecycle) Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) Hyperscaler & ecosystem partnership Partner with?Microsoft?teams across?Azure AI / Foundry,?Azure Machine Learning,?AKS / container platforms,?GPU infrastructure,?data/analytics?(e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. Align ANF's AI story with?Azure-wide AI data?guidance and reference architectures, and feed?real customer workload evidence?back into joint planning. Cross-functional leadership Lead across?engineering, product marketing, sales, customer success, and professional services?to ship capabilities and repeatable?reference architectures / proof points. Engage?strategic customers?and?design partners?to validate pain, quantify value, and de-risk roadmap bets. Market intelligence & evangelism Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate into?differentiated bets. Represent ANF as a?credible technical executive?in briefings, advisory councils, and industry forums. Industry segmentation Tailor AI storage strategy for segments where file semantics and performance matter, for example:?semiconductor/EDA,?manufacturing,?healthcare imaging,?financial services,?energy,?media & entertainment, and?HPC/simulation-including compliance and data residency realities. Job Requirements Required 10+ years?product management in?cloud infrastructure,?enterprise storage,?AI/ML infrastructure, or?data platforms?(principal scope: portfolio strategy, multi-team alignment, executive storytelling). Strong command of?enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. Hands-on familiarity with?modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. Demonstrated success influencing?engineering and partner roadmaps?without direct authority; experience with?hyperscaler first-party?or deeply partnered services is a strong plus. Excellent written and verbal communication to?customers, executives, and engineers. Preferred Direct experience with?Microsoft Azure?AI services,?GPU?estates on Azure, and/or?Azure Kubernetes Service?+ ML platform integrations. Familiarity with?Databricks,?Iceberg/Delta-class open table patterns,?Kubernetes?storage patterns,?NVIDIA AI?software stacks, and?enterprise MLOps?release cadences. Background in?regulated industries?and enterprise security/governance requirements for AI data. Education MBA?or advanced degree in?CS/Engineering?(helpful, not a substitute for demonstrated technical depth). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: . click apply for full job details
It's an exciting time to join Fisher Investments; we're investing in the future of our firm's technology and information security. Our business is growing internationally, which emphasizes the need to build an unparalleled team that promotes future global growth through strategic solutions and progress. We are important to supporting our firm's diverse businesses, and we're excited to continue solidifying that foundation as we add more experienced technologists to our Technology team. -We are looking for a Sr. Platform Engineer to design and develop solutions for enterprise data warehouses/data marts and analytics as part of a collection of systems in a large technology ecosystem. You will report to the Application Development Team Leader. -The Opportunity: -As a Sr. Platform Engineer, you will perform design and administration of our Azure data and analytics platform to ensure smooth system operations and be the guide for architectural principles and standards. As a technical expert for our data and analytics ecosystem, you will support Fisher Investments business line reporting teams and Technology Business data delivery teams. This hands-on role will provide direction to other developers of data warehouse workloads and data related projects. -You will help align department-wide solutions to Fisher's enterprise priorities and survey the technology landscape and guide the evolution of current environment to support future capabilities and requirements. You will be a part of the team that plans the technology to ensure capacity and scalability as demand and usage evolves. -The Day-to-Day: Work with business and technology partners to analyze how the data warehouse, analytics and application will meet our goals Evaluate all proposed requests to determine fit with data warehouse, analytics and application solution architecture. Own data warehouse and analytics technical platform on Azure, from implementation to enhancements and ongoing operations Design, develop and coordinate cloud data and application solutions/projects across diverse groups and areas including enterprise architecture, application development, identity and access management, network and data management Provide technical direction and guidance to database and application developers Develop cloud security and access control solution/policy, data and BI standards, guidelines and best practice for business groups and technical teams Perform maintenance and troubleshooting activities for data warehouse, BI platform and other data projects and resolve issues Monitor system application logs and identify potential issues and improvements to ensure smooth operations Perform capacity planning, cloud cost analysis and optimization and provide recommendations to management Provide after-hour system and application support Develop multiple work plans for projects and prepare appropriate status reports and submit them to managementYour Qualifications: Minimum of 8 years of experience developing software and data solution for enterprise environment focusing on data and cloud applications with direct experience with application development, data warehouse and BI design, implementation, and operation Experience with cloud data warehouse and analytics technologies, specifically Azure Databricks, Azure Synapse, Azure Data Factory, Azure Data Lake Storage, Power BI, and other Azure data solutions Knowledge or experience with infrastructure (operating system and networking) and cloud administration and automation such as Terraform for Infrastructure as Code. Knowledge of DevOps in application development such as GitHub Actions, or Powershell Experience with application development using Microsoft or cloud technologies (such as SQL, Azure, C#, Python) Experience with Machine Learning, MLOps Experience with investment management systems and processes Experience with Agile methodologies in a cloud, application, database, data warehousing and BI space BS or equivalent in Computer Science or related field or a combination of technical skills, cloud, operating system, networking, security, data application, data modeling and BI backgroundCompensation: $120,000 - $165,000 base salary per year in the state of WA. New hires should expect to start at the lower end of the range depending on experience Eligible for a discretionary bonus based on firm and individual performanceWhy Fisher Investments: -We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like: 100% paid medical, dental and vision premiums for you and your qualifying dependents A 50% 401(k) match, up to the IRS maximum 20 days of PTO, plus 10 paid holidays Family Support programs including 8 week Paid Primary Caregiver Leave, $10,000 fertility, family forming, and hormonal health assistance, and back-up child, adult, and elder care This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change. FISHER INVESTMENTS IS AN EQUAL OPPORTUNITY EMPLOYER
06/08/2026
It's an exciting time to join Fisher Investments; we're investing in the future of our firm's technology and information security. Our business is growing internationally, which emphasizes the need to build an unparalleled team that promotes future global growth through strategic solutions and progress. We are important to supporting our firm's diverse businesses, and we're excited to continue solidifying that foundation as we add more experienced technologists to our Technology team. -We are looking for a Sr. Platform Engineer to design and develop solutions for enterprise data warehouses/data marts and analytics as part of a collection of systems in a large technology ecosystem. You will report to the Application Development Team Leader. -The Opportunity: -As a Sr. Platform Engineer, you will perform design and administration of our Azure data and analytics platform to ensure smooth system operations and be the guide for architectural principles and standards. As a technical expert for our data and analytics ecosystem, you will support Fisher Investments business line reporting teams and Technology Business data delivery teams. This hands-on role will provide direction to other developers of data warehouse workloads and data related projects. -You will help align department-wide solutions to Fisher's enterprise priorities and survey the technology landscape and guide the evolution of current environment to support future capabilities and requirements. You will be a part of the team that plans the technology to ensure capacity and scalability as demand and usage evolves. -The Day-to-Day: Work with business and technology partners to analyze how the data warehouse, analytics and application will meet our goals Evaluate all proposed requests to determine fit with data warehouse, analytics and application solution architecture. Own data warehouse and analytics technical platform on Azure, from implementation to enhancements and ongoing operations Design, develop and coordinate cloud data and application solutions/projects across diverse groups and areas including enterprise architecture, application development, identity and access management, network and data management Provide technical direction and guidance to database and application developers Develop cloud security and access control solution/policy, data and BI standards, guidelines and best practice for business groups and technical teams Perform maintenance and troubleshooting activities for data warehouse, BI platform and other data projects and resolve issues Monitor system application logs and identify potential issues and improvements to ensure smooth operations Perform capacity planning, cloud cost analysis and optimization and provide recommendations to management Provide after-hour system and application support Develop multiple work plans for projects and prepare appropriate status reports and submit them to managementYour Qualifications: Minimum of 8 years of experience developing software and data solution for enterprise environment focusing on data and cloud applications with direct experience with application development, data warehouse and BI design, implementation, and operation Experience with cloud data warehouse and analytics technologies, specifically Azure Databricks, Azure Synapse, Azure Data Factory, Azure Data Lake Storage, Power BI, and other Azure data solutions Knowledge or experience with infrastructure (operating system and networking) and cloud administration and automation such as Terraform for Infrastructure as Code. Knowledge of DevOps in application development such as GitHub Actions, or Powershell Experience with application development using Microsoft or cloud technologies (such as SQL, Azure, C#, Python) Experience with Machine Learning, MLOps Experience with investment management systems and processes Experience with Agile methodologies in a cloud, application, database, data warehousing and BI space BS or equivalent in Computer Science or related field or a combination of technical skills, cloud, operating system, networking, security, data application, data modeling and BI backgroundCompensation: $120,000 - $165,000 base salary per year in the state of WA. New hires should expect to start at the lower end of the range depending on experience Eligible for a discretionary bonus based on firm and individual performanceWhy Fisher Investments: -We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like: 100% paid medical, dental and vision premiums for you and your qualifying dependents A 50% 401(k) match, up to the IRS maximum 20 days of PTO, plus 10 paid holidays Family Support programs including 8 week Paid Primary Caregiver Leave, $10,000 fertility, family forming, and hormonal health assistance, and back-up child, adult, and elder care This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change. FISHER INVESTMENTS IS AN EQUAL OPPORTUNITY EMPLOYER
It's an exciting time to join Fisher Investments; we're investing in the future of our firm's technology and information security. Our business is growing internationally, which emphasizes the need to build an unparalleled team that promotes future global growth through strategic solutions and progress. We are important to supporting our firm's diverse businesses, and we're excited to continue solidifying that foundation as we add more experienced technologists to our Technology team. -We are looking for a Sr. Platform Engineer to design and develop solutions for enterprise data warehouses/data marts and analytics as part of a collection of systems in a large technology ecosystem. You will report to the Application Development Team Leader. -The Opportunity: -As a Sr. Platform Engineer, you will perform design and administration of our Azure data and analytics platform to ensure smooth system operations and be the guide for architectural principles and standards. As a technical expert for our data and analytics ecosystem, you will support Fisher Investments business line reporting teams and Technology Business data delivery teams. This hands-on role will provide direction to other developers of data warehouse workloads and data related projects. -You will help align department-wide solutions to Fisher's enterprise priorities and survey the technology landscape and guide the evolution of current environment to support future capabilities and requirements. You will be a part of the team that plans the technology to ensure capacity and scalability as demand and usage evolves. -The Day-to-Day: Work with business and technology partners to analyze how the data warehouse, analytics and application will meet our goals Evaluate all proposed requests to determine fit with data warehouse, analytics and application solution architecture. Own data warehouse and analytics technical platform on Azure, from implementation to enhancements and ongoing operations Design, develop and coordinate cloud data and application solutions/projects across diverse groups and areas including enterprise architecture, application development, identity and access management, network and data management Provide technical direction and guidance to database and application developers Develop cloud security and access control solution/policy, data and BI standards, guidelines and best practice for business groups and technical teams Perform maintenance and troubleshooting activities for data warehouse, BI platform and other data projects and resolve issues Monitor system application logs and identify potential issues and improvements to ensure smooth operations Perform capacity planning, cloud cost analysis and optimization and provide recommendations to management Provide after-hour system and application support Develop multiple work plans for projects and prepare appropriate status reports and submit them to managementYour Qualifications: Minimum of 8 years of experience developing software and data solution for enterprise environment focusing on data and cloud applications with direct experience with application development, data warehouse and BI design, implementation, and operation Experience with cloud data warehouse and analytics technologies, specifically Azure Databricks, Azure Synapse, Azure Data Factory, Azure Data Lake Storage, Power BI, and other Azure data solutions Knowledge or experience with infrastructure (operating system and networking) and cloud administration and automation such as Terraform for Infrastructure as Code. Knowledge of DevOps in application development such as GitHub Actions, or Powershell Experience with application development using Microsoft or cloud technologies (such as SQL, Azure, C#, Python) Experience with Machine Learning, MLOps Experience with investment management systems and processes Experience with Agile methodologies in a cloud, application, database, data warehousing and BI space BS or equivalent in Computer Science or related field or a combination of technical skills, cloud, operating system, networking, security, data application, data modeling and BI backgroundWhy Fisher Investments: -We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like: 100% paid medical, dental and vision premiums for you and your qualifying dependents A 50% 401(k) match, up to the IRS maximum 20 days of PTO, plus 10 paid holidays Family Support programs including 8 week Paid Primary Caregiver Leave, $10,000 fertility, family forming, and hormonal health assistance, and back-up child, adult, and elder care This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change. FISHER INVESTMENTS IS AN EQUAL OPPORTUNITY EMPLOYER
06/08/2026
It's an exciting time to join Fisher Investments; we're investing in the future of our firm's technology and information security. Our business is growing internationally, which emphasizes the need to build an unparalleled team that promotes future global growth through strategic solutions and progress. We are important to supporting our firm's diverse businesses, and we're excited to continue solidifying that foundation as we add more experienced technologists to our Technology team. -We are looking for a Sr. Platform Engineer to design and develop solutions for enterprise data warehouses/data marts and analytics as part of a collection of systems in a large technology ecosystem. You will report to the Application Development Team Leader. -The Opportunity: -As a Sr. Platform Engineer, you will perform design and administration of our Azure data and analytics platform to ensure smooth system operations and be the guide for architectural principles and standards. As a technical expert for our data and analytics ecosystem, you will support Fisher Investments business line reporting teams and Technology Business data delivery teams. This hands-on role will provide direction to other developers of data warehouse workloads and data related projects. -You will help align department-wide solutions to Fisher's enterprise priorities and survey the technology landscape and guide the evolution of current environment to support future capabilities and requirements. You will be a part of the team that plans the technology to ensure capacity and scalability as demand and usage evolves. -The Day-to-Day: Work with business and technology partners to analyze how the data warehouse, analytics and application will meet our goals Evaluate all proposed requests to determine fit with data warehouse, analytics and application solution architecture. Own data warehouse and analytics technical platform on Azure, from implementation to enhancements and ongoing operations Design, develop and coordinate cloud data and application solutions/projects across diverse groups and areas including enterprise architecture, application development, identity and access management, network and data management Provide technical direction and guidance to database and application developers Develop cloud security and access control solution/policy, data and BI standards, guidelines and best practice for business groups and technical teams Perform maintenance and troubleshooting activities for data warehouse, BI platform and other data projects and resolve issues Monitor system application logs and identify potential issues and improvements to ensure smooth operations Perform capacity planning, cloud cost analysis and optimization and provide recommendations to management Provide after-hour system and application support Develop multiple work plans for projects and prepare appropriate status reports and submit them to managementYour Qualifications: Minimum of 8 years of experience developing software and data solution for enterprise environment focusing on data and cloud applications with direct experience with application development, data warehouse and BI design, implementation, and operation Experience with cloud data warehouse and analytics technologies, specifically Azure Databricks, Azure Synapse, Azure Data Factory, Azure Data Lake Storage, Power BI, and other Azure data solutions Knowledge or experience with infrastructure (operating system and networking) and cloud administration and automation such as Terraform for Infrastructure as Code. Knowledge of DevOps in application development such as GitHub Actions, or Powershell Experience with application development using Microsoft or cloud technologies (such as SQL, Azure, C#, Python) Experience with Machine Learning, MLOps Experience with investment management systems and processes Experience with Agile methodologies in a cloud, application, database, data warehousing and BI space BS or equivalent in Computer Science or related field or a combination of technical skills, cloud, operating system, networking, security, data application, data modeling and BI backgroundWhy Fisher Investments: -We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like: 100% paid medical, dental and vision premiums for you and your qualifying dependents A 50% 401(k) match, up to the IRS maximum 20 days of PTO, plus 10 paid holidays Family Support programs including 8 week Paid Primary Caregiver Leave, $10,000 fertility, family forming, and hormonal health assistance, and back-up child, adult, and elder care This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change. FISHER INVESTMENTS IS AN EQUAL OPPORTUNITY EMPLOYER
It's an exciting time to join Fisher Investments; we're investing in the future of our firm's technology and information security. Our business is growing internationally, which emphasizes the need to build an unparalleled team that promotes future global growth through strategic solutions and progress. We are important to supporting our firm's diverse businesses, and we're excited to continue solidifying that foundation as we add more experienced technologists to our Technology team. -We are looking for a Sr. Platform Engineer to design and develop solutions for enterprise data warehouses/data marts and analytics as part of a collection of systems in a large technology ecosystem. You will report to the Application Development Team Leader. -The Opportunity: -As a Sr. Platform Engineer, you will perform design and administration of our Azure data and analytics platform to ensure smooth system operations and be the guide for architectural principles and standards. As a technical expert for our data and analytics ecosystem, you will support Fisher Investments business line reporting teams and Technology Business data delivery teams. This hands-on role will provide direction to other developers of data warehouse workloads and data related projects. -You will help align department-wide solutions to Fisher's enterprise priorities and survey the technology landscape and guide the evolution of current environment to support future capabilities and requirements. You will be a part of the team that plans the technology to ensure capacity and scalability as demand and usage evolves. -The Day-to-Day: Work with business and technology partners to analyze how the data warehouse, analytics and application will meet our goals Evaluate all proposed requests to determine fit with data warehouse, analytics and application solution architecture. Own data warehouse and analytics technical platform on Azure, from implementation to enhancements and ongoing operations Design, develop and coordinate cloud data and application solutions/projects across diverse groups and areas including enterprise architecture, application development, identity and access management, network and data management Provide technical direction and guidance to database and application developers Develop cloud security and access control solution/policy, data and BI standards, guidelines and best practice for business groups and technical teams Perform maintenance and troubleshooting activities for data warehouse, BI platform and other data projects and resolve issues Monitor system application logs and identify potential issues and improvements to ensure smooth operations Perform capacity planning, cloud cost analysis and optimization and provide recommendations to management Provide after-hour system and application support Develop multiple work plans for projects and prepare appropriate status reports and submit them to managementYour Qualifications: Minimum of 8 years of experience developing software and data solution for enterprise environment focusing on data and cloud applications with direct experience with application development, data warehouse and BI design, implementation, and operation Experience with cloud data warehouse and analytics technologies, specifically Azure Databricks, Azure Synapse, Azure Data Factory, Azure Data Lake Storage, Power BI, and other Azure data solutions Knowledge or experience with infrastructure (operating system and networking) and cloud administration and automation such as Terraform for Infrastructure as Code. Knowledge of DevOps in application development such as GitHub Actions, or Powershell Experience with application development using Microsoft or cloud technologies (such as SQL, Azure, C#, Python) Experience with Machine Learning, MLOps Experience with investment management systems and processes Experience with Agile methodologies in a cloud, application, database, data warehousing and BI space BS or equivalent in Computer Science or related field or a combination of technical skills, cloud, operating system, networking, security, data application, data modeling and BI backgroundWhy Fisher Investments: -We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like: 100% paid medical, dental and vision premiums for you and your qualifying dependents A 50% 401(k) match, up to the IRS maximum 20 days of PTO, plus 10 paid holidays Family Support programs including 8 week Paid Primary Caregiver Leave, $10,000 fertility, family forming, and hormonal health assistance, and back-up child, adult, and elder care This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change. FISHER INVESTMENTS IS AN EQUAL OPPORTUNITY EMPLOYER
06/08/2026
It's an exciting time to join Fisher Investments; we're investing in the future of our firm's technology and information security. Our business is growing internationally, which emphasizes the need to build an unparalleled team that promotes future global growth through strategic solutions and progress. We are important to supporting our firm's diverse businesses, and we're excited to continue solidifying that foundation as we add more experienced technologists to our Technology team. -We are looking for a Sr. Platform Engineer to design and develop solutions for enterprise data warehouses/data marts and analytics as part of a collection of systems in a large technology ecosystem. You will report to the Application Development Team Leader. -The Opportunity: -As a Sr. Platform Engineer, you will perform design and administration of our Azure data and analytics platform to ensure smooth system operations and be the guide for architectural principles and standards. As a technical expert for our data and analytics ecosystem, you will support Fisher Investments business line reporting teams and Technology Business data delivery teams. This hands-on role will provide direction to other developers of data warehouse workloads and data related projects. -You will help align department-wide solutions to Fisher's enterprise priorities and survey the technology landscape and guide the evolution of current environment to support future capabilities and requirements. You will be a part of the team that plans the technology to ensure capacity and scalability as demand and usage evolves. -The Day-to-Day: Work with business and technology partners to analyze how the data warehouse, analytics and application will meet our goals Evaluate all proposed requests to determine fit with data warehouse, analytics and application solution architecture. Own data warehouse and analytics technical platform on Azure, from implementation to enhancements and ongoing operations Design, develop and coordinate cloud data and application solutions/projects across diverse groups and areas including enterprise architecture, application development, identity and access management, network and data management Provide technical direction and guidance to database and application developers Develop cloud security and access control solution/policy, data and BI standards, guidelines and best practice for business groups and technical teams Perform maintenance and troubleshooting activities for data warehouse, BI platform and other data projects and resolve issues Monitor system application logs and identify potential issues and improvements to ensure smooth operations Perform capacity planning, cloud cost analysis and optimization and provide recommendations to management Provide after-hour system and application support Develop multiple work plans for projects and prepare appropriate status reports and submit them to managementYour Qualifications: Minimum of 8 years of experience developing software and data solution for enterprise environment focusing on data and cloud applications with direct experience with application development, data warehouse and BI design, implementation, and operation Experience with cloud data warehouse and analytics technologies, specifically Azure Databricks, Azure Synapse, Azure Data Factory, Azure Data Lake Storage, Power BI, and other Azure data solutions Knowledge or experience with infrastructure (operating system and networking) and cloud administration and automation such as Terraform for Infrastructure as Code. Knowledge of DevOps in application development such as GitHub Actions, or Powershell Experience with application development using Microsoft or cloud technologies (such as SQL, Azure, C#, Python) Experience with Machine Learning, MLOps Experience with investment management systems and processes Experience with Agile methodologies in a cloud, application, database, data warehousing and BI space BS or equivalent in Computer Science or related field or a combination of technical skills, cloud, operating system, networking, security, data application, data modeling and BI backgroundWhy Fisher Investments: -We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like: 100% paid medical, dental and vision premiums for you and your qualifying dependents A 50% 401(k) match, up to the IRS maximum 20 days of PTO, plus 10 paid holidays Family Support programs including 8 week Paid Primary Caregiver Leave, $10,000 fertility, family forming, and hormonal health assistance, and back-up child, adult, and elder care This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change. FISHER INVESTMENTS IS AN EQUAL OPPORTUNITY EMPLOYER
Manager, Data Scientist - Recommendation & Personalization Systems Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives. Team Description Join an elite Applied AI team within AI Foundations, operating at the intersection of deep research and massive real-world impact. We are pioneering the next generation of personalized customer experiences across Capital One's web and mobile applications, leveraging our high-scale ML models. Our core mission involves architecting and deploying cutting-edge personalized recommendation engines. This is powered by original research into homegrown Foundation Models , advanced Reinforcement Learning techniques, and a state-of-the-art scalable architecture built for billions of interactions. Our research agenda is at the forefront of the field, actively focusing on areas such as Causal Inference , Transformer-based architectures , and sophisticated Recommender Systems . Role Description In this role, you will: Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love Leverage a broad stack of technologies - Python, Conda, AWS, H2O, Spark, and more - to reveal the insights hidden within huge volumes of numeric and textual data Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation Flex your interpersonal skills to translate the complexity of your work into tangible business goals The Ideal Candidate is: Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers. Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them. Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. A data guru. "Big data" doesn't faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science. Basic Qualifications: Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 1 year of experience performing data analytics At least 1 year of experience leveraging open source programming languages for large scale data analysis At least 1 year of experience working with machine learning At least 1 year of experience utilizing relational databases Preferred Qualifications: PhD in "STEM" field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics At least 3 years of hands-on experience building, deploying, and maintaining high-scale, production-grade ML systems using MLOps practices, including AWS, Kubeflow, and CI/CD pipelines At least 4 years of experience in developing and optimizing state-of-the-art Deep Learning models, specifically Transformer-based architectures, using PyTorch and distributed training with multi-GPU optimization At least 4 years of experience with high-performance, distributed data processing for petabyte-scale feature engineering using frameworks like DASK and PySpark Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Mgr, Data Science New York, NY: $215,200 - $245,600 for Mgr, Data Science San Jose, CA: $215,200 - $245,600 for Mgr, Data Science Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
06/08/2026
Full time
Manager, Data Scientist - Recommendation & Personalization Systems Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives. Team Description Join an elite Applied AI team within AI Foundations, operating at the intersection of deep research and massive real-world impact. We are pioneering the next generation of personalized customer experiences across Capital One's web and mobile applications, leveraging our high-scale ML models. Our core mission involves architecting and deploying cutting-edge personalized recommendation engines. This is powered by original research into homegrown Foundation Models , advanced Reinforcement Learning techniques, and a state-of-the-art scalable architecture built for billions of interactions. Our research agenda is at the forefront of the field, actively focusing on areas such as Causal Inference , Transformer-based architectures , and sophisticated Recommender Systems . Role Description In this role, you will: Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love Leverage a broad stack of technologies - Python, Conda, AWS, H2O, Spark, and more - to reveal the insights hidden within huge volumes of numeric and textual data Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation Flex your interpersonal skills to translate the complexity of your work into tangible business goals The Ideal Candidate is: Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers. Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them. Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. A data guru. "Big data" doesn't faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science. Basic Qualifications: Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 1 year of experience performing data analytics At least 1 year of experience leveraging open source programming languages for large scale data analysis At least 1 year of experience working with machine learning At least 1 year of experience utilizing relational databases Preferred Qualifications: PhD in "STEM" field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics At least 3 years of hands-on experience building, deploying, and maintaining high-scale, production-grade ML systems using MLOps practices, including AWS, Kubeflow, and CI/CD pipelines At least 4 years of experience in developing and optimizing state-of-the-art Deep Learning models, specifically Transformer-based architectures, using PyTorch and distributed training with multi-GPU optimization At least 4 years of experience with high-performance, distributed data processing for petabyte-scale feature engineering using frameworks like DASK and PySpark Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Mgr, Data Science New York, NY: $215,200 - $245,600 for Mgr, Data Science San Jose, CA: $215,200 - $245,600 for Mgr, Data Science Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Manager, Product Management - Enterprise AI/ML Product Management at Capital One is a booming, vibrant craft that requires reimagining the status quo, finding value creation opportunities, and driving innovative and sustainable customer experiences through technology. We believe our portfolio of businesses and investments in growth and transformation will result in a company with the scale, brand, capabilities, talent, and values to succeed as the digital revolution transforms our society and our industry. About the Team This team powers real-time, intelligent banking by safely introducing, scaling, and operationalizing new Artificial Intelligence and Machine Learning capabilities across the business. Capital One Product Framework In this role, you'll be expected to demonstrate proficiency in five key areas which we consider to be the foundation for successful Product management: Human Centered - Obsesses about internal and external customer needs to reimagine and innovate product solutions Business Focused - Delivers game-changing outcomes by focusing on leverage and execution excellence Technology Driven - Leverages technology to deliver innovative and resilient solutions that enable both near term and long term value Integrated Problem Solving - Identifies and resolves complex problems to deliver outcomes while mitigating product risks Transformational Leadership - Leads cross functional teams to solve customer problems and drive organizational alignment Basic Qualifications: At least 3 years of experience working in Product Management Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) or an MBA with a quantitative concentration Preferred Qualifications: Experience in product management for technical platforms (data science or Machine Learning preferred), MLOps, or internal developer tools. Familiarity with cloud computing environments (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Kubernetes). Demonstrated experience working in an Agile environment and managing a product backlog. At this time, Capital One will not sponsor a new applicant for employment authorization for this position The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $164,800 - $188,100 for Manager, Product Management Chicago, IL: $149,800 - $171,000 for Manager, Product Management McLean, VA: $164,800 - $188,100 for Manager, Product Management New York, NY: $179,700 - $205,100 for Manager, Product Management Richmond, VA: $149,800 - $171,000 for Manager, Product Management San Francisco, CA: $179,700 - $205,100 for Manager, Product Management Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
06/08/2026
Full time
Manager, Product Management - Enterprise AI/ML Product Management at Capital One is a booming, vibrant craft that requires reimagining the status quo, finding value creation opportunities, and driving innovative and sustainable customer experiences through technology. We believe our portfolio of businesses and investments in growth and transformation will result in a company with the scale, brand, capabilities, talent, and values to succeed as the digital revolution transforms our society and our industry. About the Team This team powers real-time, intelligent banking by safely introducing, scaling, and operationalizing new Artificial Intelligence and Machine Learning capabilities across the business. Capital One Product Framework In this role, you'll be expected to demonstrate proficiency in five key areas which we consider to be the foundation for successful Product management: Human Centered - Obsesses about internal and external customer needs to reimagine and innovate product solutions Business Focused - Delivers game-changing outcomes by focusing on leverage and execution excellence Technology Driven - Leverages technology to deliver innovative and resilient solutions that enable both near term and long term value Integrated Problem Solving - Identifies and resolves complex problems to deliver outcomes while mitigating product risks Transformational Leadership - Leads cross functional teams to solve customer problems and drive organizational alignment Basic Qualifications: At least 3 years of experience working in Product Management Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) or an MBA with a quantitative concentration Preferred Qualifications: Experience in product management for technical platforms (data science or Machine Learning preferred), MLOps, or internal developer tools. Familiarity with cloud computing environments (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Kubernetes). Demonstrated experience working in an Agile environment and managing a product backlog. At this time, Capital One will not sponsor a new applicant for employment authorization for this position The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $164,800 - $188,100 for Manager, Product Management Chicago, IL: $149,800 - $171,000 for Manager, Product Management McLean, VA: $164,800 - $188,100 for Manager, Product Management New York, NY: $179,700 - $205,100 for Manager, Product Management Richmond, VA: $149,800 - $171,000 for Manager, Product Management San Francisco, CA: $179,700 - $205,100 for Manager, Product Management Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Manager, Product Management - Enterprise AI/ML Product Management at Capital One is a booming, vibrant craft that requires reimagining the status quo, finding value creation opportunities, and driving innovative and sustainable customer experiences through technology. We believe our portfolio of businesses and investments in growth and transformation will result in a company with the scale, brand, capabilities, talent, and values to succeed as the digital revolution transforms our society and our industry. About the Team This team powers real-time, intelligent banking by safely introducing, scaling, and operationalizing new Artificial Intelligence and Machine Learning capabilities across the business. Capital One Product Framework In this role, you'll be expected to demonstrate proficiency in five key areas which we consider to be the foundation for successful Product management: Human Centered - Obsesses about internal and external customer needs to reimagine and innovate product solutions Business Focused - Delivers game-changing outcomes by focusing on leverage and execution excellence Technology Driven - Leverages technology to deliver innovative and resilient solutions that enable both near term and long term value Integrated Problem Solving - Identifies and resolves complex problems to deliver outcomes while mitigating product risks Transformational Leadership - Leads cross functional teams to solve customer problems and drive organizational alignment Basic Qualifications: At least 3 years of experience working in Product Management Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) or an MBA with a quantitative concentration Preferred Qualifications: Experience in product management for technical platforms (data science or Machine Learning preferred), MLOps, or internal developer tools. Familiarity with cloud computing environments (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Kubernetes). Demonstrated experience working in an Agile environment and managing a product backlog. At this time, Capital One will not sponsor a new applicant for employment authorization for this position The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $164,800 - $188,100 for Manager, Product Management Chicago, IL: $149,800 - $171,000 for Manager, Product Management McLean, VA: $164,800 - $188,100 for Manager, Product Management New York, NY: $179,700 - $205,100 for Manager, Product Management Richmond, VA: $149,800 - $171,000 for Manager, Product Management San Francisco, CA: $179,700 - $205,100 for Manager, Product Management Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
06/08/2026
Full time
Manager, Product Management - Enterprise AI/ML Product Management at Capital One is a booming, vibrant craft that requires reimagining the status quo, finding value creation opportunities, and driving innovative and sustainable customer experiences through technology. We believe our portfolio of businesses and investments in growth and transformation will result in a company with the scale, brand, capabilities, talent, and values to succeed as the digital revolution transforms our society and our industry. About the Team This team powers real-time, intelligent banking by safely introducing, scaling, and operationalizing new Artificial Intelligence and Machine Learning capabilities across the business. Capital One Product Framework In this role, you'll be expected to demonstrate proficiency in five key areas which we consider to be the foundation for successful Product management: Human Centered - Obsesses about internal and external customer needs to reimagine and innovate product solutions Business Focused - Delivers game-changing outcomes by focusing on leverage and execution excellence Technology Driven - Leverages technology to deliver innovative and resilient solutions that enable both near term and long term value Integrated Problem Solving - Identifies and resolves complex problems to deliver outcomes while mitigating product risks Transformational Leadership - Leads cross functional teams to solve customer problems and drive organizational alignment Basic Qualifications: At least 3 years of experience working in Product Management Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) or an MBA with a quantitative concentration Preferred Qualifications: Experience in product management for technical platforms (data science or Machine Learning preferred), MLOps, or internal developer tools. Familiarity with cloud computing environments (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Kubernetes). Demonstrated experience working in an Agile environment and managing a product backlog. At this time, Capital One will not sponsor a new applicant for employment authorization for this position The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $164,800 - $188,100 for Manager, Product Management Chicago, IL: $149,800 - $171,000 for Manager, Product Management McLean, VA: $164,800 - $188,100 for Manager, Product Management New York, NY: $179,700 - $205,100 for Manager, Product Management Richmond, VA: $149,800 - $171,000 for Manager, Product Management San Francisco, CA: $179,700 - $205,100 for Manager, Product Management Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Manager, Product Management - Enterprise AI/ML Product Management at Capital One is a booming, vibrant craft that requires reimagining the status quo, finding value creation opportunities, and driving innovative and sustainable customer experiences through technology. We believe our portfolio of businesses and investments in growth and transformation will result in a company with the scale, brand, capabilities, talent, and values to succeed as the digital revolution transforms our society and our industry. About the Team This team powers real-time, intelligent banking by safely introducing, scaling, and operationalizing new Artificial Intelligence and Machine Learning capabilities across the business. Capital One Product Framework In this role, you'll be expected to demonstrate proficiency in five key areas which we consider to be the foundation for successful Product management: Human Centered - Obsesses about internal and external customer needs to reimagine and innovate product solutions Business Focused - Delivers game-changing outcomes by focusing on leverage and execution excellence Technology Driven - Leverages technology to deliver innovative and resilient solutions that enable both near term and long term value Integrated Problem Solving - Identifies and resolves complex problems to deliver outcomes while mitigating product risks Transformational Leadership - Leads cross functional teams to solve customer problems and drive organizational alignment Basic Qualifications: At least 3 years of experience working in Product Management Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) or an MBA with a quantitative concentration Preferred Qualifications: Experience in product management for technical platforms (data science or Machine Learning preferred), MLOps, or internal developer tools. Familiarity with cloud computing environments (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Kubernetes). Demonstrated experience working in an Agile environment and managing a product backlog. At this time, Capital One will not sponsor a new applicant for employment authorization for this position The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $164,800 - $188,100 for Manager, Product Management Chicago, IL: $149,800 - $171,000 for Manager, Product Management McLean, VA: $164,800 - $188,100 for Manager, Product Management New York, NY: $179,700 - $205,100 for Manager, Product Management Richmond, VA: $149,800 - $171,000 for Manager, Product Management San Francisco, CA: $179,700 - $205,100 for Manager, Product Management Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
06/08/2026
Full time
Manager, Product Management - Enterprise AI/ML Product Management at Capital One is a booming, vibrant craft that requires reimagining the status quo, finding value creation opportunities, and driving innovative and sustainable customer experiences through technology. We believe our portfolio of businesses and investments in growth and transformation will result in a company with the scale, brand, capabilities, talent, and values to succeed as the digital revolution transforms our society and our industry. About the Team This team powers real-time, intelligent banking by safely introducing, scaling, and operationalizing new Artificial Intelligence and Machine Learning capabilities across the business. Capital One Product Framework In this role, you'll be expected to demonstrate proficiency in five key areas which we consider to be the foundation for successful Product management: Human Centered - Obsesses about internal and external customer needs to reimagine and innovate product solutions Business Focused - Delivers game-changing outcomes by focusing on leverage and execution excellence Technology Driven - Leverages technology to deliver innovative and resilient solutions that enable both near term and long term value Integrated Problem Solving - Identifies and resolves complex problems to deliver outcomes while mitigating product risks Transformational Leadership - Leads cross functional teams to solve customer problems and drive organizational alignment Basic Qualifications: At least 3 years of experience working in Product Management Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Computer Engineering, Software Engineering, Mechanical Engineering, Information Systems or a related quantitative field) or an MBA with a quantitative concentration Preferred Qualifications: Experience in product management for technical platforms (data science or Machine Learning preferred), MLOps, or internal developer tools. Familiarity with cloud computing environments (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Kubernetes). Demonstrated experience working in an Agile environment and managing a product backlog. At this time, Capital One will not sponsor a new applicant for employment authorization for this position The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $164,800 - $188,100 for Manager, Product Management Chicago, IL: $149,800 - $171,000 for Manager, Product Management McLean, VA: $164,800 - $188,100 for Manager, Product Management New York, NY: $179,700 - $205,100 for Manager, Product Management Richmond, VA: $149,800 - $171,000 for Manager, Product Management San Francisco, CA: $179,700 - $205,100 for Manager, Product Management Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Job DescriptionJob Description Revolutional delivers advanced technology solutions and mission support to federal agencies across civilian, health, and national security environments. We apply modern capabilities, including AI/ML, cloud, cybersecurity, and IT modernization to solve complex challenges, enable faster and more secure operations, and drive measurable mission outcomes. We are redefining how federal technology gets built and delivered by operating with a product mindset, prioritizing speed, ownership, and execution over bureaucracy. Lead Data Scientist Location: Suitland, MD (Hybrid) Terms: Full-time Clearance/Work Authorization: U.S. Citizenship with the ability to obtain and maintain a Public Trust is required Travel: % Project Description This position supports Revolutional's federal customer as part of an application transformation and modernization initiative. This program is driving a large-scale transformation of systems into a data-centric, cloud-native ecosystem capable of supporting high-volume, near real-time data processing and advanced analytics. The work includes modernization of legacy applications, development of new cloud-native solutions, and implementation of DevSecOps and scaled Agile practices across the organization. The core challenge: orchestrating complex, multi-contractor delivery while transforming both technology and operating models without disrupting mission-critical operations. Position Description As a Lead Data Scientist at Revolutional, you will define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program. You will lead efforts spanning advanced analytics, machine learning, MLOps, AI governance, and operational analytics integration across complex enterprise systems. This role requires close collaboration with data engineering, architecture, application development, and operational teams to ensure AI/ML capabilities are production-ready, scalable, explainable, and integrated into enterprise workflows. You will operate at both strategic and hands-on levels guiding technical direction, developing advanced models, and ensuring analytics solutions deliver measurable mission impact. Responsibilities Provide technical leadership across enterprise data science and AI/ML initiatives within a large-scale modernization program Design, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production environments Lead implementation of MLOps practices supporting model lifecycle management, automation, observability, and continuous improvement Apply advanced data science techniques including NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analysis Design and support event-driven analytics and real-time/streaming ML pipelines Collaborate with data engineers, architects, application teams, and SMEs to integrate AI/ML capabilities into enterprise systems and operational workflows Support system-of-systems (SoS) integrations across multiple systems, vendors, contractors, and interdependent platforms Establish AI governance frameworks supporting fairness, bias mitigation, explainability, transparency, and compliance with standards such as the NIST AI Risk Management Framework Develop reproducible analytics workflows, technical documentation, analysis plans, dashboards, and reporting deliverables Support DataOps and Agile data science practices including iterative development, pipeline automation, CI/CD integration, and collaborative model delivery Ensure analytics solutions align with enterprise security, privacy, and compliance requirements Drive improvements in data quality, validation, accessibility, and operational analytics reliability Present findings, recommendations, and technical approaches to executive leadership and stakeholders Mentor data scientists and analytics teams while promoting best practices across the organization Technical Environment Cloud-native AI/ML and analytics environments (AWS, Azure) Distributed data platforms and enterprise analytics ecosystems Python, R, Spark, TensorFlow, PyTorch, Databricks, and related ML frameworks MLOps pipelines, model deployment platforms, and automation frameworks Real-time streaming and event-driven analytics systems DevSecOps pipelines and CI/CD automation practices DataOps and Agile/SAFe delivery environments APIs, distributed integrations, and enterprise data platforms Collaboration and delivery tools (Git, Jira, Confluence) High-volume, near real-time processing environments What You Bring (Requirements) Baseline Requirements U.S. Citizenship with the ability to obtain a Public Trust PhD in Data Science, Computer Science, Statistics, Mathematics, or related field 15+ years of experience in data science, AI/ML, advanced analytics, or enterprise modernization initiatives Proven experience leading AI/ML and analytics efforts across large-scale, complex systems Ability to obtain and maintain a Public Trust clearance Technical Capabilities Deep expertise in machine learning, statistical modeling, and advanced analytics techniques Experience implementing end-to-end MLOps pipelines including model training, validation, deployment, monitoring, and scaling Experience with NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analytics Experience designing and supporting real-time or streaming analytics architectures Experience integrating AI/ML solutions across system-of-systems (SoS) environments and distributed enterprise platforms Experience implementing AI governance frameworks addressing explainability, fairness, transparency, and bias mitigation Experience with large-scale distributed data environments and cloud-native analytics platforms Experience with DataOps, CI/CD integration, and Agile/SAFe delivery models Strong experience with Python, R, Spark, TensorFlow, PyTorch, Databricks, and related AI/ML technologies Experience developing dashboards, technical reports, analysis plans, and reproducible analytics workflows Experience collaborating across engineering, architecture, application, and operational teams at enterprise scale Core Strengths Strong ownership mindset with accountability for enterprise AI/ML outcomes Ability to translate complex analytical findings into actionable business and mission insights Strategic thinker capable of balancing innovation, governance, and operational realities Effective communication across technical, operational, and executive stakeholders Strong leadership and mentoring capabilities across data science and analytics teams Ability to operate in complex, fast-moving, multi-contractor delivery environments Nice to Have (Differentiators) Experience supporting statistical and similarly large-scale federal modernization programs Experience implementing enterprise AI governance or responsible AI initiatives Experience with event-driven architectures, streaming analytics, or operational AI systems Experience supporting large-scale data modernization or enterprise analytics transformation efforts Experience working within DevSecOps-enabled AI/ML delivery environments _ Here at Revolutional we are pleased to have been repeatedly recognized for our outstanding work culture, the innovative work we do, and the employees on our team who make a difference each day. Some of these recognitions include: Recognized as a Top 20 "Best Place to Work in Virginia" Recipient of Department of Labor's HireVets Gold Medallion Great Place to Work Certification for five years running A Virginia Chamber of Commerce Fantastic 50 company A Northern Virginia Technology Council Tech 100 company Inc. 5000 list of fastest growing companies for eleven years Two-time SBA SBIR Tibbett's Award winner Virginia Values Veterans (V3) Certification We recognize that every bit of our success is the result of our teams of hard-working, motivated, and innovative professionals who are proud to call themselves part of the Revolutional family! In addition to competitive compensation, a family-focused culture, and a dynamic, productive work environment, we offer all full-time employees a variety of benefits including, but not limited to Traditional and HSA- eligible medical insurance plans 100% employer-paid dental and vision insurance options 100% employer-sponsored STD, LTD, and life insurance 5% 401(k) company matching Flexible-schedules and teleworking options Paid holidays and PTO Accrual Plans Paid Parental Leave Professional development and career growth opportunities Team and company-wide events, recognition, and appreciation and so much more! Check out our Revolutional LinkedIn to find out a little more about who we are and if we are the right next step for your career! Revolutional is an Equal Opportunity Employer providing equal employment opportunity to all employees and applicants for employment without regard to race, color, religion, national origin, age, gender . click apply for full job details
06/08/2026
Full time
Job DescriptionJob Description Revolutional delivers advanced technology solutions and mission support to federal agencies across civilian, health, and national security environments. We apply modern capabilities, including AI/ML, cloud, cybersecurity, and IT modernization to solve complex challenges, enable faster and more secure operations, and drive measurable mission outcomes. We are redefining how federal technology gets built and delivered by operating with a product mindset, prioritizing speed, ownership, and execution over bureaucracy. Lead Data Scientist Location: Suitland, MD (Hybrid) Terms: Full-time Clearance/Work Authorization: U.S. Citizenship with the ability to obtain and maintain a Public Trust is required Travel: % Project Description This position supports Revolutional's federal customer as part of an application transformation and modernization initiative. This program is driving a large-scale transformation of systems into a data-centric, cloud-native ecosystem capable of supporting high-volume, near real-time data processing and advanced analytics. The work includes modernization of legacy applications, development of new cloud-native solutions, and implementation of DevSecOps and scaled Agile practices across the organization. The core challenge: orchestrating complex, multi-contractor delivery while transforming both technology and operating models without disrupting mission-critical operations. Position Description As a Lead Data Scientist at Revolutional, you will define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program. You will lead efforts spanning advanced analytics, machine learning, MLOps, AI governance, and operational analytics integration across complex enterprise systems. This role requires close collaboration with data engineering, architecture, application development, and operational teams to ensure AI/ML capabilities are production-ready, scalable, explainable, and integrated into enterprise workflows. You will operate at both strategic and hands-on levels guiding technical direction, developing advanced models, and ensuring analytics solutions deliver measurable mission impact. Responsibilities Provide technical leadership across enterprise data science and AI/ML initiatives within a large-scale modernization program Design, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production environments Lead implementation of MLOps practices supporting model lifecycle management, automation, observability, and continuous improvement Apply advanced data science techniques including NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analysis Design and support event-driven analytics and real-time/streaming ML pipelines Collaborate with data engineers, architects, application teams, and SMEs to integrate AI/ML capabilities into enterprise systems and operational workflows Support system-of-systems (SoS) integrations across multiple systems, vendors, contractors, and interdependent platforms Establish AI governance frameworks supporting fairness, bias mitigation, explainability, transparency, and compliance with standards such as the NIST AI Risk Management Framework Develop reproducible analytics workflows, technical documentation, analysis plans, dashboards, and reporting deliverables Support DataOps and Agile data science practices including iterative development, pipeline automation, CI/CD integration, and collaborative model delivery Ensure analytics solutions align with enterprise security, privacy, and compliance requirements Drive improvements in data quality, validation, accessibility, and operational analytics reliability Present findings, recommendations, and technical approaches to executive leadership and stakeholders Mentor data scientists and analytics teams while promoting best practices across the organization Technical Environment Cloud-native AI/ML and analytics environments (AWS, Azure) Distributed data platforms and enterprise analytics ecosystems Python, R, Spark, TensorFlow, PyTorch, Databricks, and related ML frameworks MLOps pipelines, model deployment platforms, and automation frameworks Real-time streaming and event-driven analytics systems DevSecOps pipelines and CI/CD automation practices DataOps and Agile/SAFe delivery environments APIs, distributed integrations, and enterprise data platforms Collaboration and delivery tools (Git, Jira, Confluence) High-volume, near real-time processing environments What You Bring (Requirements) Baseline Requirements U.S. Citizenship with the ability to obtain a Public Trust PhD in Data Science, Computer Science, Statistics, Mathematics, or related field 15+ years of experience in data science, AI/ML, advanced analytics, or enterprise modernization initiatives Proven experience leading AI/ML and analytics efforts across large-scale, complex systems Ability to obtain and maintain a Public Trust clearance Technical Capabilities Deep expertise in machine learning, statistical modeling, and advanced analytics techniques Experience implementing end-to-end MLOps pipelines including model training, validation, deployment, monitoring, and scaling Experience with NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analytics Experience designing and supporting real-time or streaming analytics architectures Experience integrating AI/ML solutions across system-of-systems (SoS) environments and distributed enterprise platforms Experience implementing AI governance frameworks addressing explainability, fairness, transparency, and bias mitigation Experience with large-scale distributed data environments and cloud-native analytics platforms Experience with DataOps, CI/CD integration, and Agile/SAFe delivery models Strong experience with Python, R, Spark, TensorFlow, PyTorch, Databricks, and related AI/ML technologies Experience developing dashboards, technical reports, analysis plans, and reproducible analytics workflows Experience collaborating across engineering, architecture, application, and operational teams at enterprise scale Core Strengths Strong ownership mindset with accountability for enterprise AI/ML outcomes Ability to translate complex analytical findings into actionable business and mission insights Strategic thinker capable of balancing innovation, governance, and operational realities Effective communication across technical, operational, and executive stakeholders Strong leadership and mentoring capabilities across data science and analytics teams Ability to operate in complex, fast-moving, multi-contractor delivery environments Nice to Have (Differentiators) Experience supporting statistical and similarly large-scale federal modernization programs Experience implementing enterprise AI governance or responsible AI initiatives Experience with event-driven architectures, streaming analytics, or operational AI systems Experience supporting large-scale data modernization or enterprise analytics transformation efforts Experience working within DevSecOps-enabled AI/ML delivery environments _ Here at Revolutional we are pleased to have been repeatedly recognized for our outstanding work culture, the innovative work we do, and the employees on our team who make a difference each day. Some of these recognitions include: Recognized as a Top 20 "Best Place to Work in Virginia" Recipient of Department of Labor's HireVets Gold Medallion Great Place to Work Certification for five years running A Virginia Chamber of Commerce Fantastic 50 company A Northern Virginia Technology Council Tech 100 company Inc. 5000 list of fastest growing companies for eleven years Two-time SBA SBIR Tibbett's Award winner Virginia Values Veterans (V3) Certification We recognize that every bit of our success is the result of our teams of hard-working, motivated, and innovative professionals who are proud to call themselves part of the Revolutional family! In addition to competitive compensation, a family-focused culture, and a dynamic, productive work environment, we offer all full-time employees a variety of benefits including, but not limited to Traditional and HSA- eligible medical insurance plans 100% employer-paid dental and vision insurance options 100% employer-sponsored STD, LTD, and life insurance 5% 401(k) company matching Flexible-schedules and teleworking options Paid holidays and PTO Accrual Plans Paid Parental Leave Professional development and career growth opportunities Team and company-wide events, recognition, and appreciation and so much more! Check out our Revolutional LinkedIn to find out a little more about who we are and if we are the right next step for your career! Revolutional is an Equal Opportunity Employer providing equal employment opportunity to all employees and applicants for employment without regard to race, color, religion, national origin, age, gender . click apply for full job details
Job DescriptionJob Description Revolutional delivers advanced technology solutions and mission support to federal agencies across civilian, health, and national security environments. We apply modern capabilities, including AI/ML, cloud, cybersecurity, and IT modernization to solve complex challenges, enable faster and more secure operations, and drive measurable mission outcomes. We are redefining how federal technology gets built and delivered by operating with a product mindset, prioritizing speed, ownership, and execution over bureaucracy. Lead Data Scientist Location: Suitland, MD (Hybrid) Terms: Full-time Clearance/Work Authorization: U.S. Citizenship with the ability to obtain and maintain a Public Trust is required Travel: % Project Description This position supports Revolutional's federal customer as part of an application transformation and modernization initiative. This program is driving a large-scale transformation of systems into a data-centric, cloud-native ecosystem capable of supporting high-volume, near real-time data processing and advanced analytics. The work includes modernization of legacy applications, development of new cloud-native solutions, and implementation of DevSecOps and scaled Agile practices across the organization. The core challenge: orchestrating complex, multi-contractor delivery while transforming both technology and operating models without disrupting mission-critical operations. Position Description As a Lead Data Scientist at Revolutional, you will define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program. You will lead efforts spanning advanced analytics, machine learning, MLOps, AI governance, and operational analytics integration across complex enterprise systems. This role requires close collaboration with data engineering, architecture, application development, and operational teams to ensure AI/ML capabilities are production-ready, scalable, explainable, and integrated into enterprise workflows. You will operate at both strategic and hands-on levels guiding technical direction, developing advanced models, and ensuring analytics solutions deliver measurable mission impact. Responsibilities Provide technical leadership across enterprise data science and AI/ML initiatives within a large-scale modernization program Design, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production environments Lead implementation of MLOps practices supporting model lifecycle management, automation, observability, and continuous improvement Apply advanced data science techniques including NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analysis Design and support event-driven analytics and real-time/streaming ML pipelines Collaborate with data engineers, architects, application teams, and SMEs to integrate AI/ML capabilities into enterprise systems and operational workflows Support system-of-systems (SoS) integrations across multiple systems, vendors, contractors, and interdependent platforms Establish AI governance frameworks supporting fairness, bias mitigation, explainability, transparency, and compliance with standards such as the NIST AI Risk Management Framework Develop reproducible analytics workflows, technical documentation, analysis plans, dashboards, and reporting deliverables Support DataOps and Agile data science practices including iterative development, pipeline automation, CI/CD integration, and collaborative model delivery Ensure analytics solutions align with enterprise security, privacy, and compliance requirements Drive improvements in data quality, validation, accessibility, and operational analytics reliability Present findings, recommendations, and technical approaches to executive leadership and stakeholders Mentor data scientists and analytics teams while promoting best practices across the organization Technical Environment Cloud-native AI/ML and analytics environments (AWS, Azure) Distributed data platforms and enterprise analytics ecosystems Python, R, Spark, TensorFlow, PyTorch, Databricks, and related ML frameworks MLOps pipelines, model deployment platforms, and automation frameworks Real-time streaming and event-driven analytics systems DevSecOps pipelines and CI/CD automation practices DataOps and Agile/SAFe delivery environments APIs, distributed integrations, and enterprise data platforms Collaboration and delivery tools (Git, Jira, Confluence) High-volume, near real-time processing environments What You Bring (Requirements) Baseline Requirements U.S. Citizenship with the ability to obtain a Public Trust PhD in Data Science, Computer Science, Statistics, Mathematics, or related field 15+ years of experience in data science, AI/ML, advanced analytics, or enterprise modernization initiatives Proven experience leading AI/ML and analytics efforts across large-scale, complex systems Ability to obtain and maintain a Public Trust clearance Technical Capabilities Deep expertise in machine learning, statistical modeling, and advanced analytics techniques Experience implementing end-to-end MLOps pipelines including model training, validation, deployment, monitoring, and scaling Experience with NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analytics Experience designing and supporting real-time or streaming analytics architectures Experience integrating AI/ML solutions across system-of-systems (SoS) environments and distributed enterprise platforms Experience implementing AI governance frameworks addressing explainability, fairness, transparency, and bias mitigation Experience with large-scale distributed data environments and cloud-native analytics platforms Experience with DataOps, CI/CD integration, and Agile/SAFe delivery models Strong experience with Python, R, Spark, TensorFlow, PyTorch, Databricks, and related AI/ML technologies Experience developing dashboards, technical reports, analysis plans, and reproducible analytics workflows Experience collaborating across engineering, architecture, application, and operational teams at enterprise scale Core Strengths Strong ownership mindset with accountability for enterprise AI/ML outcomes Ability to translate complex analytical findings into actionable business and mission insights Strategic thinker capable of balancing innovation, governance, and operational realities Effective communication across technical, operational, and executive stakeholders Strong leadership and mentoring capabilities across data science and analytics teams Ability to operate in complex, fast-moving, multi-contractor delivery environments Nice to Have (Differentiators) Experience supporting statistical and similarly large-scale federal modernization programs Experience implementing enterprise AI governance or responsible AI initiatives Experience with event-driven architectures, streaming analytics, or operational AI systems Experience supporting large-scale data modernization or enterprise analytics transformation efforts Experience working within DevSecOps-enabled AI/ML delivery environments _ Here at Revolutional we are pleased to have been repeatedly recognized for our outstanding work culture, the innovative work we do, and the employees on our team who make a difference each day. Some of these recognitions include: Recognized as a Top 20 "Best Place to Work in Virginia" Recipient of Department of Labor's HireVets Gold Medallion Great Place to Work Certification for five years running A Virginia Chamber of Commerce Fantastic 50 company A Northern Virginia Technology Council Tech 100 company Inc. 5000 list of fastest growing companies for eleven years Two-time SBA SBIR Tibbett's Award winner Virginia Values Veterans (V3) Certification We recognize that every bit of our success is the result of our teams of hard-working, motivated, and innovative professionals who are proud to call themselves part of the Revolutional family! In addition to competitive compensation, a family-focused culture, and a dynamic, productive work environment, we offer all full-time employees a variety of benefits including, but not limited to Traditional and HSA- eligible medical insurance plans 100% employer-paid dental and vision insurance options 100% employer-sponsored STD, LTD, and life insurance 5% 401(k) company matching Flexible-schedules and teleworking options Paid holidays and PTO Accrual Plans Paid Parental Leave Professional development and career growth opportunities Team and company-wide events, recognition, and appreciation and so much more! Check out our Revolutional LinkedIn to find out a little more about who we are and if we are the right next step for your career! Revolutional is an Equal Opportunity Employer providing equal employment opportunity to all employees and applicants for employment without regard to race, color, religion, national origin, age, gender . click apply for full job details
06/08/2026
Full time
Job DescriptionJob Description Revolutional delivers advanced technology solutions and mission support to federal agencies across civilian, health, and national security environments. We apply modern capabilities, including AI/ML, cloud, cybersecurity, and IT modernization to solve complex challenges, enable faster and more secure operations, and drive measurable mission outcomes. We are redefining how federal technology gets built and delivered by operating with a product mindset, prioritizing speed, ownership, and execution over bureaucracy. Lead Data Scientist Location: Suitland, MD (Hybrid) Terms: Full-time Clearance/Work Authorization: U.S. Citizenship with the ability to obtain and maintain a Public Trust is required Travel: % Project Description This position supports Revolutional's federal customer as part of an application transformation and modernization initiative. This program is driving a large-scale transformation of systems into a data-centric, cloud-native ecosystem capable of supporting high-volume, near real-time data processing and advanced analytics. The work includes modernization of legacy applications, development of new cloud-native solutions, and implementation of DevSecOps and scaled Agile practices across the organization. The core challenge: orchestrating complex, multi-contractor delivery while transforming both technology and operating models without disrupting mission-critical operations. Position Description As a Lead Data Scientist at Revolutional, you will define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program. You will lead efforts spanning advanced analytics, machine learning, MLOps, AI governance, and operational analytics integration across complex enterprise systems. This role requires close collaboration with data engineering, architecture, application development, and operational teams to ensure AI/ML capabilities are production-ready, scalable, explainable, and integrated into enterprise workflows. You will operate at both strategic and hands-on levels guiding technical direction, developing advanced models, and ensuring analytics solutions deliver measurable mission impact. Responsibilities Provide technical leadership across enterprise data science and AI/ML initiatives within a large-scale modernization program Design, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production environments Lead implementation of MLOps practices supporting model lifecycle management, automation, observability, and continuous improvement Apply advanced data science techniques including NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analysis Design and support event-driven analytics and real-time/streaming ML pipelines Collaborate with data engineers, architects, application teams, and SMEs to integrate AI/ML capabilities into enterprise systems and operational workflows Support system-of-systems (SoS) integrations across multiple systems, vendors, contractors, and interdependent platforms Establish AI governance frameworks supporting fairness, bias mitigation, explainability, transparency, and compliance with standards such as the NIST AI Risk Management Framework Develop reproducible analytics workflows, technical documentation, analysis plans, dashboards, and reporting deliverables Support DataOps and Agile data science practices including iterative development, pipeline automation, CI/CD integration, and collaborative model delivery Ensure analytics solutions align with enterprise security, privacy, and compliance requirements Drive improvements in data quality, validation, accessibility, and operational analytics reliability Present findings, recommendations, and technical approaches to executive leadership and stakeholders Mentor data scientists and analytics teams while promoting best practices across the organization Technical Environment Cloud-native AI/ML and analytics environments (AWS, Azure) Distributed data platforms and enterprise analytics ecosystems Python, R, Spark, TensorFlow, PyTorch, Databricks, and related ML frameworks MLOps pipelines, model deployment platforms, and automation frameworks Real-time streaming and event-driven analytics systems DevSecOps pipelines and CI/CD automation practices DataOps and Agile/SAFe delivery environments APIs, distributed integrations, and enterprise data platforms Collaboration and delivery tools (Git, Jira, Confluence) High-volume, near real-time processing environments What You Bring (Requirements) Baseline Requirements U.S. Citizenship with the ability to obtain a Public Trust PhD in Data Science, Computer Science, Statistics, Mathematics, or related field 15+ years of experience in data science, AI/ML, advanced analytics, or enterprise modernization initiatives Proven experience leading AI/ML and analytics efforts across large-scale, complex systems Ability to obtain and maintain a Public Trust clearance Technical Capabilities Deep expertise in machine learning, statistical modeling, and advanced analytics techniques Experience implementing end-to-end MLOps pipelines including model training, validation, deployment, monitoring, and scaling Experience with NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analytics Experience designing and supporting real-time or streaming analytics architectures Experience integrating AI/ML solutions across system-of-systems (SoS) environments and distributed enterprise platforms Experience implementing AI governance frameworks addressing explainability, fairness, transparency, and bias mitigation Experience with large-scale distributed data environments and cloud-native analytics platforms Experience with DataOps, CI/CD integration, and Agile/SAFe delivery models Strong experience with Python, R, Spark, TensorFlow, PyTorch, Databricks, and related AI/ML technologies Experience developing dashboards, technical reports, analysis plans, and reproducible analytics workflows Experience collaborating across engineering, architecture, application, and operational teams at enterprise scale Core Strengths Strong ownership mindset with accountability for enterprise AI/ML outcomes Ability to translate complex analytical findings into actionable business and mission insights Strategic thinker capable of balancing innovation, governance, and operational realities Effective communication across technical, operational, and executive stakeholders Strong leadership and mentoring capabilities across data science and analytics teams Ability to operate in complex, fast-moving, multi-contractor delivery environments Nice to Have (Differentiators) Experience supporting statistical and similarly large-scale federal modernization programs Experience implementing enterprise AI governance or responsible AI initiatives Experience with event-driven architectures, streaming analytics, or operational AI systems Experience supporting large-scale data modernization or enterprise analytics transformation efforts Experience working within DevSecOps-enabled AI/ML delivery environments _ Here at Revolutional we are pleased to have been repeatedly recognized for our outstanding work culture, the innovative work we do, and the employees on our team who make a difference each day. Some of these recognitions include: Recognized as a Top 20 "Best Place to Work in Virginia" Recipient of Department of Labor's HireVets Gold Medallion Great Place to Work Certification for five years running A Virginia Chamber of Commerce Fantastic 50 company A Northern Virginia Technology Council Tech 100 company Inc. 5000 list of fastest growing companies for eleven years Two-time SBA SBIR Tibbett's Award winner Virginia Values Veterans (V3) Certification We recognize that every bit of our success is the result of our teams of hard-working, motivated, and innovative professionals who are proud to call themselves part of the Revolutional family! In addition to competitive compensation, a family-focused culture, and a dynamic, productive work environment, we offer all full-time employees a variety of benefits including, but not limited to Traditional and HSA- eligible medical insurance plans 100% employer-paid dental and vision insurance options 100% employer-sponsored STD, LTD, and life insurance 5% 401(k) company matching Flexible-schedules and teleworking options Paid holidays and PTO Accrual Plans Paid Parental Leave Professional development and career growth opportunities Team and company-wide events, recognition, and appreciation and so much more! Check out our Revolutional LinkedIn to find out a little more about who we are and if we are the right next step for your career! Revolutional is an Equal Opportunity Employer providing equal employment opportunity to all employees and applicants for employment without regard to race, color, religion, national origin, age, gender . click apply for full job details
Job DescriptionJob Description Revolutional delivers advanced technology solutions and mission support to federal agencies across civilian, health, and national security environments. We apply modern capabilities, including AI/ML, cloud, cybersecurity, and IT modernization to solve complex challenges, enable faster and more secure operations, and drive measurable mission outcomes. We are redefining how federal technology gets built and delivered by operating with a product mindset, prioritizing speed, ownership, and execution over bureaucracy. Lead Data Scientist Location: Suitland, MD (Hybrid) Terms: Full-time Clearance/Work Authorization: U.S. Citizenship with the ability to obtain and maintain a Public Trust is required Travel: % Project Description This position supports Revolutional's federal customer as part of an application transformation and modernization initiative. This program is driving a large-scale transformation of systems into a data-centric, cloud-native ecosystem capable of supporting high-volume, near real-time data processing and advanced analytics. The work includes modernization of legacy applications, development of new cloud-native solutions, and implementation of DevSecOps and scaled Agile practices across the organization. The core challenge: orchestrating complex, multi-contractor delivery while transforming both technology and operating models without disrupting mission-critical operations. Position Description As a Lead Data Scientist at Revolutional, you will define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program. You will lead efforts spanning advanced analytics, machine learning, MLOps, AI governance, and operational analytics integration across complex enterprise systems. This role requires close collaboration with data engineering, architecture, application development, and operational teams to ensure AI/ML capabilities are production-ready, scalable, explainable, and integrated into enterprise workflows. You will operate at both strategic and hands-on levels guiding technical direction, developing advanced models, and ensuring analytics solutions deliver measurable mission impact. Responsibilities Provide technical leadership across enterprise data science and AI/ML initiatives within a large-scale modernization program Design, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production environments Lead implementation of MLOps practices supporting model lifecycle management, automation, observability, and continuous improvement Apply advanced data science techniques including NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analysis Design and support event-driven analytics and real-time/streaming ML pipelines Collaborate with data engineers, architects, application teams, and SMEs to integrate AI/ML capabilities into enterprise systems and operational workflows Support system-of-systems (SoS) integrations across multiple systems, vendors, contractors, and interdependent platforms Establish AI governance frameworks supporting fairness, bias mitigation, explainability, transparency, and compliance with standards such as the NIST AI Risk Management Framework Develop reproducible analytics workflows, technical documentation, analysis plans, dashboards, and reporting deliverables Support DataOps and Agile data science practices including iterative development, pipeline automation, CI/CD integration, and collaborative model delivery Ensure analytics solutions align with enterprise security, privacy, and compliance requirements Drive improvements in data quality, validation, accessibility, and operational analytics reliability Present findings, recommendations, and technical approaches to executive leadership and stakeholders Mentor data scientists and analytics teams while promoting best practices across the organization Technical Environment Cloud-native AI/ML and analytics environments (AWS, Azure) Distributed data platforms and enterprise analytics ecosystems Python, R, Spark, TensorFlow, PyTorch, Databricks, and related ML frameworks MLOps pipelines, model deployment platforms, and automation frameworks Real-time streaming and event-driven analytics systems DevSecOps pipelines and CI/CD automation practices DataOps and Agile/SAFe delivery environments APIs, distributed integrations, and enterprise data platforms Collaboration and delivery tools (Git, Jira, Confluence) High-volume, near real-time processing environments What You Bring (Requirements) Baseline Requirements U.S. Citizenship with the ability to obtain a Public Trust PhD in Data Science, Computer Science, Statistics, Mathematics, or related field 15+ years of experience in data science, AI/ML, advanced analytics, or enterprise modernization initiatives Proven experience leading AI/ML and analytics efforts across large-scale, complex systems Ability to obtain and maintain a Public Trust clearance Technical Capabilities Deep expertise in machine learning, statistical modeling, and advanced analytics techniques Experience implementing end-to-end MLOps pipelines including model training, validation, deployment, monitoring, and scaling Experience with NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analytics Experience designing and supporting real-time or streaming analytics architectures Experience integrating AI/ML solutions across system-of-systems (SoS) environments and distributed enterprise platforms Experience implementing AI governance frameworks addressing explainability, fairness, transparency, and bias mitigation Experience with large-scale distributed data environments and cloud-native analytics platforms Experience with DataOps, CI/CD integration, and Agile/SAFe delivery models Strong experience with Python, R, Spark, TensorFlow, PyTorch, Databricks, and related AI/ML technologies Experience developing dashboards, technical reports, analysis plans, and reproducible analytics workflows Experience collaborating across engineering, architecture, application, and operational teams at enterprise scale Core Strengths Strong ownership mindset with accountability for enterprise AI/ML outcomes Ability to translate complex analytical findings into actionable business and mission insights Strategic thinker capable of balancing innovation, governance, and operational realities Effective communication across technical, operational, and executive stakeholders Strong leadership and mentoring capabilities across data science and analytics teams Ability to operate in complex, fast-moving, multi-contractor delivery environments Nice to Have (Differentiators) Experience supporting statistical and similarly large-scale federal modernization programs Experience implementing enterprise AI governance or responsible AI initiatives Experience with event-driven architectures, streaming analytics, or operational AI systems Experience supporting large-scale data modernization or enterprise analytics transformation efforts Experience working within DevSecOps-enabled AI/ML delivery environments _ Here at Revolutional we are pleased to have been repeatedly recognized for our outstanding work culture, the innovative work we do, and the employees on our team who make a difference each day. Some of these recognitions include: Recognized as a Top 20 "Best Place to Work in Virginia" Recipient of Department of Labor's HireVets Gold Medallion Great Place to Work Certification for five years running A Virginia Chamber of Commerce Fantastic 50 company A Northern Virginia Technology Council Tech 100 company Inc. 5000 list of fastest growing companies for eleven years Two-time SBA SBIR Tibbett's Award winner Virginia Values Veterans (V3) Certification We recognize that every bit of our success is the result of our teams of hard-working, motivated, and innovative professionals who are proud to call themselves part of the Revolutional family! In addition to competitive compensation, a family-focused culture, and a dynamic, productive work environment, we offer all full-time employees a variety of benefits including, but not limited to Traditional and HSA- eligible medical insurance plans 100% employer-paid dental and vision insurance options 100% employer-sponsored STD, LTD, and life insurance 5% 401(k) company matching Flexible-schedules and teleworking options Paid holidays and PTO Accrual Plans Paid Parental Leave Professional development and career growth opportunities Team and company-wide events, recognition, and appreciation and so much more! Check out our Revolutional LinkedIn to find out a little more about who we are and if we are the right next step for your career! Revolutional is an Equal Opportunity Employer providing equal employment opportunity to all employees and applicants for employment without regard to race, color, religion, national origin, age, gender . click apply for full job details
06/08/2026
Full time
Job DescriptionJob Description Revolutional delivers advanced technology solutions and mission support to federal agencies across civilian, health, and national security environments. We apply modern capabilities, including AI/ML, cloud, cybersecurity, and IT modernization to solve complex challenges, enable faster and more secure operations, and drive measurable mission outcomes. We are redefining how federal technology gets built and delivered by operating with a product mindset, prioritizing speed, ownership, and execution over bureaucracy. Lead Data Scientist Location: Suitland, MD (Hybrid) Terms: Full-time Clearance/Work Authorization: U.S. Citizenship with the ability to obtain and maintain a Public Trust is required Travel: % Project Description This position supports Revolutional's federal customer as part of an application transformation and modernization initiative. This program is driving a large-scale transformation of systems into a data-centric, cloud-native ecosystem capable of supporting high-volume, near real-time data processing and advanced analytics. The work includes modernization of legacy applications, development of new cloud-native solutions, and implementation of DevSecOps and scaled Agile practices across the organization. The core challenge: orchestrating complex, multi-contractor delivery while transforming both technology and operating models without disrupting mission-critical operations. Position Description As a Lead Data Scientist at Revolutional, you will define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program. You will lead efforts spanning advanced analytics, machine learning, MLOps, AI governance, and operational analytics integration across complex enterprise systems. This role requires close collaboration with data engineering, architecture, application development, and operational teams to ensure AI/ML capabilities are production-ready, scalable, explainable, and integrated into enterprise workflows. You will operate at both strategic and hands-on levels guiding technical direction, developing advanced models, and ensuring analytics solutions deliver measurable mission impact. Responsibilities Provide technical leadership across enterprise data science and AI/ML initiatives within a large-scale modernization program Design, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production environments Lead implementation of MLOps practices supporting model lifecycle management, automation, observability, and continuous improvement Apply advanced data science techniques including NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analysis Design and support event-driven analytics and real-time/streaming ML pipelines Collaborate with data engineers, architects, application teams, and SMEs to integrate AI/ML capabilities into enterprise systems and operational workflows Support system-of-systems (SoS) integrations across multiple systems, vendors, contractors, and interdependent platforms Establish AI governance frameworks supporting fairness, bias mitigation, explainability, transparency, and compliance with standards such as the NIST AI Risk Management Framework Develop reproducible analytics workflows, technical documentation, analysis plans, dashboards, and reporting deliverables Support DataOps and Agile data science practices including iterative development, pipeline automation, CI/CD integration, and collaborative model delivery Ensure analytics solutions align with enterprise security, privacy, and compliance requirements Drive improvements in data quality, validation, accessibility, and operational analytics reliability Present findings, recommendations, and technical approaches to executive leadership and stakeholders Mentor data scientists and analytics teams while promoting best practices across the organization Technical Environment Cloud-native AI/ML and analytics environments (AWS, Azure) Distributed data platforms and enterprise analytics ecosystems Python, R, Spark, TensorFlow, PyTorch, Databricks, and related ML frameworks MLOps pipelines, model deployment platforms, and automation frameworks Real-time streaming and event-driven analytics systems DevSecOps pipelines and CI/CD automation practices DataOps and Agile/SAFe delivery environments APIs, distributed integrations, and enterprise data platforms Collaboration and delivery tools (Git, Jira, Confluence) High-volume, near real-time processing environments What You Bring (Requirements) Baseline Requirements U.S. Citizenship with the ability to obtain a Public Trust PhD in Data Science, Computer Science, Statistics, Mathematics, or related field 15+ years of experience in data science, AI/ML, advanced analytics, or enterprise modernization initiatives Proven experience leading AI/ML and analytics efforts across large-scale, complex systems Ability to obtain and maintain a Public Trust clearance Technical Capabilities Deep expertise in machine learning, statistical modeling, and advanced analytics techniques Experience implementing end-to-end MLOps pipelines including model training, validation, deployment, monitoring, and scaling Experience with NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analytics Experience designing and supporting real-time or streaming analytics architectures Experience integrating AI/ML solutions across system-of-systems (SoS) environments and distributed enterprise platforms Experience implementing AI governance frameworks addressing explainability, fairness, transparency, and bias mitigation Experience with large-scale distributed data environments and cloud-native analytics platforms Experience with DataOps, CI/CD integration, and Agile/SAFe delivery models Strong experience with Python, R, Spark, TensorFlow, PyTorch, Databricks, and related AI/ML technologies Experience developing dashboards, technical reports, analysis plans, and reproducible analytics workflows Experience collaborating across engineering, architecture, application, and operational teams at enterprise scale Core Strengths Strong ownership mindset with accountability for enterprise AI/ML outcomes Ability to translate complex analytical findings into actionable business and mission insights Strategic thinker capable of balancing innovation, governance, and operational realities Effective communication across technical, operational, and executive stakeholders Strong leadership and mentoring capabilities across data science and analytics teams Ability to operate in complex, fast-moving, multi-contractor delivery environments Nice to Have (Differentiators) Experience supporting statistical and similarly large-scale federal modernization programs Experience implementing enterprise AI governance or responsible AI initiatives Experience with event-driven architectures, streaming analytics, or operational AI systems Experience supporting large-scale data modernization or enterprise analytics transformation efforts Experience working within DevSecOps-enabled AI/ML delivery environments _ Here at Revolutional we are pleased to have been repeatedly recognized for our outstanding work culture, the innovative work we do, and the employees on our team who make a difference each day. Some of these recognitions include: Recognized as a Top 20 "Best Place to Work in Virginia" Recipient of Department of Labor's HireVets Gold Medallion Great Place to Work Certification for five years running A Virginia Chamber of Commerce Fantastic 50 company A Northern Virginia Technology Council Tech 100 company Inc. 5000 list of fastest growing companies for eleven years Two-time SBA SBIR Tibbett's Award winner Virginia Values Veterans (V3) Certification We recognize that every bit of our success is the result of our teams of hard-working, motivated, and innovative professionals who are proud to call themselves part of the Revolutional family! In addition to competitive compensation, a family-focused culture, and a dynamic, productive work environment, we offer all full-time employees a variety of benefits including, but not limited to Traditional and HSA- eligible medical insurance plans 100% employer-paid dental and vision insurance options 100% employer-sponsored STD, LTD, and life insurance 5% 401(k) company matching Flexible-schedules and teleworking options Paid holidays and PTO Accrual Plans Paid Parental Leave Professional development and career growth opportunities Team and company-wide events, recognition, and appreciation and so much more! Check out our Revolutional LinkedIn to find out a little more about who we are and if we are the right next step for your career! Revolutional is an Equal Opportunity Employer providing equal employment opportunity to all employees and applicants for employment without regard to race, color, religion, national origin, age, gender . click apply for full job details
