18 jobs found in Richmond

Job Description Job Description Phoenix Cyber is looking for Cybersecurity Engineers with a Networking/Firewall focus to join our client delivery team. Requirements: Degree in a STEM related discipline and/or a minimum 5 years of experience IT certifications such as CySA, CEH, CISSP, Security X/CASP, GCIH Experience with F5 Web Application Firewalls (WAF) Security clearance required Nice to have: Demonstrated proficiency in cyber security platforms: SOAR, SIEM, IDS/IPS, DLP, WAF, Endpoint Security Linux administration experience Cloud infrastructure experience (AWS, Google, or Azure) Responsibilities: Configure, apply, test, maintain, and optimize WAF policies supporting websites and applications. Work with application and website stakeholders to identify security requirements and develop appropriate WAF policies. Perform operational and preventive maintenance, troubleshooting, patching, upgrades, and configuration changes for WAF environments. Analyze application security requirements and recommend appropriate security controls and configurations. Support testing of WAF policies and security configurations prior to production implementation. Troubleshoot application and WAF issues and identify corrective actions to improve system reliability and security. Support system security testing, vulnerability remediation, and compliance activities. Provide technical recommendations regarding WAF capabilities, enhancements, and emerging technologies. Provide technical briefings, documentation, and training materials as required. Phoenix Cyber is a national provider of cybersecurity engineering services, operations services, sustainment services and managed security services to organizations determined to strengthen their security posture and enhance the processes and technology used by their security operations team. Phoenix Cyber is an equal opportunity employer and complies with Executive Order 11246, Section 503 of the Rehabilitation Act of 1973, the Vietnam Era Veteran's Readjustment Assistance Act (VEVRAA), all amendments to these regulations, and applicable executive orders, federal, and state regulations. Applicants are considered without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, and/or veteran status. Phoenix Cyber participates in E-Verify to confirm the employment eligibility of all newly-hired employees. To learn more about E-Verify, including your rights and responsibilities, go to Powered by JazzHR D2tO0buRyR

Machine Learning Engineer 4 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities. In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). 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: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 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).

Machine Learning Engineer 5 (Senior Manager, IC) In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities. In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value. Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 6 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences 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: $229,900 - $262,400 for Machine Learning Engineer 5 Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5 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).

Director, AI Engineer (Remote Eligible) At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. Capital One is open to hiring a Remote Employee for this opportunity. What You'll Do: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Oversee the design, development, testing, deployment, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more. Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. Attract and retain top talent in the AI industry and nurture personal and professional development for your team. Foster a culture of learning and staying abreast of the state-of-the-art in AI. Translate the enterprise AI strategy into portfolio-level execution plans across multiple product areas, balancing innovation with delivery discipline Scale AI engineering practices across teams through shared infrastructure, reusable components, and unified observability and governance frameworks Establish enterprise standard for Responsible AI, including fairness metrics, model evaluation protocols, documentation requirements, and audit readiness Partner with research, compliance, and enterprise risk teams to ensure deployed systems meet emerging ethical and regulatory standards The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production You get fulfillment from empowering others to achieve their potential and you actively drive professional development through mentoring and coaching. You are hands-on when necessary and lead by example You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enables you to see and exploit optimization opportunities that others miss You are a resilient trailblazer who can forge new paths to achieve business goals when the route is unknown Basic Qualifications: Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies At least 3 years of people leadership experience Preferred Qualifications: 5+ years of experience managing and leading an engineering team 7+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang Experience in building agentic AI systems and agentic workflows Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Experience building and leading multi-team AI organization delivering multiple enterprise products or capabilities concurrently Proven ability to expand and execute long-term AI platform strategies aligned to enterprise priorities and regulatory frameworks Experience establishing cross-functional operating rhythms and review cadences (OKRs, AI governance councils, quarterly reviews) Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs) 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. Remote (Regardless of Location): $244,700 - $279,200 for Director, AI Engineer Cambridge, MA: $269,100 - $307,200 for Director, AI Engineer McLean, VA: $269,100 - $307,200 for Director, AI Engineer New York, NY: $293,600 - $335,100 for Director, AI Engineer Richmond, VA: $244,700 - $279,200 for Director, AI Engineer San Francisco, CA: $293,600 - $335,100 for Director, AI Engineer San Jose, CA: $293,600 - $335,100 for Director, AI Engineer 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 . click apply for full job details

Sr. Staff AI Engineer At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. What You'll Do: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more. Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. Define and steer the technical AI architecture vision, integrating applied research breakthroughs into production ecosystems with reliability and scale Lead the establishment of AI performance, safety, and transparency standards that guide all model development and deployment company-wide Drive multi-year platform initiatives that unify data, compute and model lifecycle management under and cohesive enterprise AI architecture Mentor senior technical leaders across research, data and engineering disciplines, developing the next generation of Capital One's AI technical leadership Basic Qualifications: Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 10 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies At least 10 years of experience programming with Python, Go, Scala, CUDA, or Java Preferred Qualifications: Experience architecting AI platforms with tradeoff decisions around cost, latency, throughput and accuracy 9 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the SVP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Experience in building agentic AI systems and agentic workflows Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Recognized industry leader in applied AI or machine learning infrastructure through patents, publications, or open-source leadership Demonstrated experience designing long-term AI infrastructure strategies - balancing cost, scale, ethics and regulatory compliance Experience driving organization-wide adoption of AI safety, alignment and governance standards, collaborating with policy, risk and legal teams Proven ability to shape R&D investment strategy by identifying breakthrough AI capabilities with material business impact Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs) 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. Cambridge, MA: $314,800 - $359,300 for Sr. Staff AI Engineer McLean, VA: $314,800 - $359,300 for Sr. Staff AI Engineer New York, NY: $343,400 - $392,000 for Sr. Staff AI Engineer Richmond, VA: $286,200 - $326,700 for Sr. Staff AI Engineer San Francisco, CA: $343,400 - $392,000 for Sr. Staff AI Engineer San Jose, CA: $343,400 - $392,000 for Sr. Staff AI Engineer 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).

Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). 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 Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 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).

Machine Learning Engineer 5 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 6 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences 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: $229,900 - $262,400 for Machine Learning Engineer 5 New York, NY: $250,800 - $286,200 for Machine Learning Engineer 5 Plano, TX: $209,000 - $238,500 for Machine Learning Engineer 5 Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5 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).

Machine Learning Engineer 5 (IC) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 6 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences 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: $229,900 - $262,400 for Machine Learning Engineer 5 New York, NY: $250,800 - $286,200 for Machine Learning Engineer 5 Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5 San Francisco, CA: $250,800 - $286,200 for Machine Learning Engineer 5 San Jose, CA: $250,800 - $286,200 for Machine Learning Engineer 5 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).

Machine Learning Engineer 3 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 3 years of experience programming with Python, Java, Golang, or C++ At least 2 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 3 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 1 year of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 1+ years of experience optimizing ML algorithms, configurations, and infrastructure 1+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 1+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 1+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) Authored/co-authored a paper on a ML technique, model, or proof of concept 1+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). 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: $161,800 - $184,600 for Machine Learning Engineer 3 New York, NY: $176,500 - $201,400 for Machine Learning Engineer 3 Plano, TX: $147,100 - $167,900 for Machine Learning Engineer 3 Richmond, VA: $147,100 - $167,900 for Machine Learning Engineer 3 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).

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine exceptional products for our customers. In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities. In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products that change how our associates work and provide value to our customers. Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation and experimentation, large language model inference, similarity search, guardrails, governance, observability and agentic AI. Fine-tune, develop and evaluate machine learning and foundation models, Collaborate as part of a cross-functional Agile team to create and enhance software that utilizes state-of-the-art AI and ML capabilities Contribute thought leadership and technical vision to the long term roadmap of pioneering AI systems at Capital One. Leverage a broad stack of Open Source and SaaS AI technologies. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues. Retrain, maintain, and monitor models in production. Construct optimized data pipelines to feed ML models. Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. You are a passionate communicator, comfortable with explaining complex technical concepts to non-technical partners across the business, sometimes in front of large audiences. Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. Passion for staying abreast of the latest AI research and AI systems, and judiciously applying novel techniques in production Strategic & Business-Oriented: You think beyond the technology, deeply understanding business needs and how AI can solve them. You don't just build; you strategize and prioritize work that delivers the greatest business value. Highly Collaborative & Transparent: You are a natural partner, working seamlessly across engineering, product, and data science teams. You communicate your progress, blockers, and decisions clearly and proactively, ensuring everyone is aligned. You love sharing knowledge and insights and are committed to the success of the entire team. Technically Mature & Humble: You possess a strong foundation in engineering and mathematics. You are a resilient problem-solver who can bring clarity to complex, undefined problems and articulate your findings concisely. You demonstrate professional maturity by committing to and executing on team decisions. Flexible & Fungible: You are eager to roll up your sleeves and contribute wherever the team needs you most. You are comfortable working across different aspects of the tech stack and adapting to evolving priorities. A Lifelong Learner: You love staying current with the latest AI research and can apply novel techniques to production systems judiciously, always with a focus on business impact. Basic Qualifications: Bachelor's Degree At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field 7+ years of experience designing, developing, delivering, and supporting AI services at scale 3+ years of experience building production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 3+ years of experience developing AI and ML algorithms or technologies using Python 2+ years of experience with Retrieval Augmented Generation (RAG) 2+ years of experience with data gathering and preparation for ML models 2+ years of people leader experience 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, Azure Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). 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: $197,300 - $225,100 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer Richmond, VA: $179,400 - $204,700 for Lead Machine Learning Engineer 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 . click apply for full job details

AI Engineer 5 (Gen AI Platform Services) Overview: At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. What You'll Do: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more. Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. Design, implement and optimize multi-model orchestration pipelines - integrating LLMs, vector search, and domain-specific models into unified systems Establish and lead cost-performance governance reviews across AI systems, tracking GPU utilization, model throughput, and inference cost efficiency Lead team design councils or design review boards to ensure technical consistency and compliance with AI engineering standards Mentor Principal and Manager-level AI engineers, fostering cross-domain learning and elevating organizational technical maturity The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enables you to see and exploit optimization opportunities that others miss You are a resilient trailblazer who can forge new paths to achieve business goals when the route is unknown Basic Qualifications: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java Preferred Qualifications: Experience leading development AI systems with tradeoff decisions around cost, latency, throughput and accuracy 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience designing, developing, delivering, and supporting complex AI systems Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Experience in building agentic AI systems and agentic workflows Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Experience architecting and integrating heterogeneous AI systems - including rule-based, retrieval-augmented, and generative components - into unified production pipelines Experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review processes Demonstrated ability to balance model performance and operational cost through dynamic inference strategies and model compression Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs) 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. New York, NY: $250,800 - $286,200 for AI Engineer 5 Richmond, VA: $209,000 - $238,500 for AI Engineer 5 San Francisco, CA: $250,800 - $286,200 for AI Engineer 5 San Jose, CA: $250,800 - $286,200 for AI Engineer 5 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 . click apply for full job details

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. Key Responsibilities GCP Foundation, Landing Zones & Infrastructure-as-Code Cloud Architecture & Automation: Design, build, and maintain enterprise GCP infrastructure using Terraform/Terragrunt, enforcing Infrastructure-as-Code (IaC) and GitOps practices for all cloud environments. Landing Zone Governance: Own the configuration and security baseline of GCP Organization structures, Projects, Folders, Resource Hierarchies, and IAM roles/permissions using least-privilege principles. Network & Hybrid Connectivity: Configure and maintain GCP Shared VPCs, VPC Service Controls (VPC-SC), Cloud Interconnect, Cloud VPN, Cloud DNS, Firewall Rules, and load balancers to support secure, low-latency hybrid connectivity between GCP services, Apigee X, and Oracle Exadata / JDE ERP environments. Apigee X & Oracle Exadata / JDE ERP Integration Support Apigee X Platform Engineering: Provision, configure, and maintain Apigee X runtime environments, API gateways, PSC (Private Service Connect) attachments, and load balancers in accordance with enterprise security baselines. Oracle Exadata & JDE Interconnect: Engineer and maintain high-performance, resilient hybrid network topology (Cloud Interconnect/Partner Interconnect) linking GCP applications and Apigee gateways directly to Oracle Exadata database systems and JDE backend services. API Security & Traffic Controls: Work alongside Integration Architects to enforce mTLS, VPC Service Controls, rate limiting, and secure endpoints across Apigee X, GCP microservices, and Exadata/JDE integration boundaries. Containerization, Compute & Serverless GKE & Kubernetes Management: Deploy, secure, and operate Google Kubernetes Engine (GKE) clusters powering enterprise Spring Boot microservices, AI workloads, and internal developer platforms. Serverless Execution: Configure and support serverless workloads using Cloud Run, Cloud Functions, and App Engine, optimizing performance and latency for transactional database and API flows. Platform Reliability: Build automated deployment pipelines using GitHub Actions or Cloud Build to support seamless infrastructure provisioning and application cutovers. FinOps & Cloud Cost Optimization Cost Allocation & Visibility: Establish FinOps standards across GCP projects, implementing label/tagging enforcement, budget alerts, and showback/chargeback reporting for business units. Resource Tuning: Analyze resource utilization (Compute Engine, GKE, Cloud SQL, BigQuery) to right-size instances, optimize committed use discounts (CUDs), and eliminate unused infrastructure. Security, Compliance & Observability Cloud Security Posture: Implement CISO security standards, including GCP Secret Manager, KMS, mTLS, SAST/DAST container vulnerability scanning, and binary authorization. Observability Baseline: Build and maintain centralized logging, monitoring, and distributed tracing across all GCP environments, Apigee instances, and Exadata/JDE connectivity paths using Google Cloud Operations Suite (Cloud Logging, Cloud Monitoring, Cloud Trace). Minimum Requirements Experience: 10+ years of enterprise IT/infrastructure experience, with at least 4+ years dedicated to designing, engineering, and operating production environments on Google Cloud Platform (GCP). Apigee & Database/ERP Interconnect: Hands-on experience provisioning and supporting Apigee X / Apigee Edge environments and managing hybrid interconnectivity to high-performance database infrastructure (Oracle Exadata) and enterprise ERP systems (JD Edwards). IaC & Automation Mastery: Expert-level hands-on skills with Terraform, Git, and CI/CD pipelines (GitHub Actions, Cloud Build, or GitLab CI). Kubernetes & Containers: Strong experience deploying and managing production Google Kubernetes Engine (GKE) clusters and Docker containerized applications. GCP Networking & Security: Deep understanding of GCP VPC networking, Shared VPCs, Private Service Connect (PSC), VPC Service Controls, Cloud Interconnect, IAM, and GCP security controls. Scripting: Proficiency in Python, Bash, or Go for cloud automation and tooling development. FinOps Understanding: Practical experience implementing cloud cost optimization, quota management, and resource right-sizing strategies. Technical Skills Google Cloud Services: GKE, Cloud Run, Cloud SQL, BigQuery, Cloud Storage, VPC Service Controls, Secret Manager, IAM, Cloud Logging, Cloud Monitoring, Cloud Trace. API Management & Hybrid Database/ERP: Apigee X, Apigee Edge, Private Service Connect (PSC), Cloud Interconnect, Oracle Exadata database connectivity, JD Edwards (JDE) hybrid interconnectivity. Infrastructure-as-Code: Terraform, Terragrunt, Helm, Docker, Kubernetes. Networking & Security: Shared VPC, Cloud VPN, Cloud Interconnect, Firewalls, mTLS, OAuth 2.0, KMS, Least Privilege IAM. CI/CD & DevOps: GitHub Actions, Cloud Build, GitHub Advanced Security (GHAS), Git, Linux/Unix administration. Scripting & Tooling: Python, Bash, Go, gcloud CLI. Certifications & Education Certifications: Google Professional Cloud Architect or Google Professional Cloud DevOps Engineer (required or to be obtained within 6 months). Education: Bachelor's degree in Computer Science, Information Technology, Engineering, or a related technical field, or equivalent practical experience. About McKesson Medical-Surgical McKesson Medical-Surgical (MMS), which is expected to become Wellverse in early 2027, is a subsidiary and publicly reported segment of the McKesson Corporation. MMS distributes medical-surgical supplies, pharmaceuticals, diagnostic equipment and supplies, along with other solutions and services to virtually every type of healthcare setting and provider outside of the traditional hospital. These markets - often referred to as Alternate Care or Non-Acute Care - include physician offices, surgery centers, long-term care providers, laboratories, home health and hospice agencies, health systems, government facilities and online marketplaces and retailers. Alternate Care markets are growing rapidly and MMS is proud to be a leader in this space. With a team of approximately 8,000 employees, a network of 15 distribution centers and approximately 900 delivery vehicles, we collaborate with more than 2,200 leading manufacturers and serve over 200,000 customer accounts across the U.S. Our catalog includes more than 270,000 SKUs of branded and private-label medical-surgical products - from bandages to specialty pharmaceuticals and COVID-19 tests. We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $115,300 - $192,100 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities . click apply for full job details

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. Position Summary McKesson Corporation is seeking talented Software Developer to join our Supply Chain Technology team with a strong background in microservices architecture, system integrations, CP4D, Apache Kafka, and Artificial Intelligence (AI) . This role is ideal for someone with 4+ years of hands-on development experience who thrives in a fast-paced, innovation-driven environment. You will be responsible for designing and implementing scalable, intelligent systems that power modern enterprise applications and data-driven decision-making. Key Responsibilities Architect, develop, and maintain microservices-based applications using modern frameworks and cloud-native technologies. Design and implement real-time data streaming solutions using Apache Kafka. Build robust integrations between internal systems and third-party platforms using APIs, messaging queues, and middleware. Collaborate with AI/ML teams to embed intelligent features into applications, including predictive analytics, NLP, and automation. Develop and maintain ETL pipelines using CP4D. Write and optimize SQL and PL/SQL queries, stored procedures, and triggers in Oracle. Ensure high performance, scalability, and reliability of distributed systems. Participate in agile development processes, including sprint planning, code reviews, and CI/CD. Monitor and troubleshoot production systems, ensuring uptime and responsiveness. Minimum Qualifications Typically requires 4+ years of relevant experience. Critical Skills Strong proficiency in programming languages such as Java, JavaScript, Python, or Go. Hands-on experience with microservices architecture and containerization (Docker, Kubernetes). Experience with cloud platforms (GCP, Azure, AWS). Proficient in ETL development, Oracle performance tuning, and complex SQL. Deep understanding of Apache Kafka for event-driven and real-time data processing. Experience implementing AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn. Strong problem-solving skills and ability to work independently and in teams Preferred Attributes Experience in healthcare or supply chain domain. Strong analytical and problem-solving skills. Knowledge of API gateways, service mesh, and observability tools. Experience with MLOps and deploying AI models in production environments. Certifications in cloud, Kafka, or AI technologies. Exposure to data engineering and big data tools (e.g., Spark, Flink). Education Bachelor's degree in computer science, Engineering, or a related field (or equivalent experience). About MMS McKesson Medical-Surgical (MMS), which is expected to become Wellverse in early 2027, is a subsidiary and publicly reported segment of the McKesson Corporation. MMS distributes medical-surgical supplies, pharmaceuticals, diagnostic equipment and supplies, along with other solutions and services to virtually every type of healthcare setting and provider outside of the traditional hospital. These markets - often referred to as Alternate Care or Non-Acute Care - include physician offices, surgery centers, long-term care providers, laboratories, home health and hospice agencies, health systems, government facilities and online marketplaces and retailers. Alternate Care markets are growing rapidly, and MMS is proud to be a leader in this space. With a team of approximately 8,000 employees, a network of 15 distribution centers and approximately 900 delivery vehicles, we collaborate with more than 2,200 leading manufacturers and serve over 200,000 customer accounts across the U.S. Our catalog includes more than 270,000 SKUs of branded and private-label medical-surgical products - from bandages to specialty pharmaceuticals and COVID-19 tests. We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $103,500 - $172,500 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

Retail Odyssey is seeking a detail-oriented and strategically minded Retail Data Collector to join our innovative retail operations team. As a Retail Data Collector, you will be vital in optimizing merchandising and supply chain operations by gathering critical data directly from the sales floor. Your insights will assist in enhancing store performance and driving significant sales growth. Working closely with planogram analysts and the merchandising team, you will ensure that our retail solutions are executed with precision, maintaining our commitment to our partners nationwide. Responsibilities Collect, record, and maintain accurate retail data regarding sales figures, inventory levels, and customer engagement metrics. Work with the merchandising team to implement and refine planograms based on data findings. Report discrepancies and adjustments to ensure database and display accuracy. Regularly audit in-store promotions and pricing for compliance and competitiveness. Collaborate with the retail analytics team to contribute data-driven insights for strategic planning. Required Skills Strong analytical abilities and attention to detail Proficient in Microsoft Excel and data collection software Excellent organizational and time-management skills Ability to work independently and as part of a team Good communication skills to report findings effectively

Job Description Job Description Description: Hybrid 3 days onsite in Henrico, VA Our client is a leading Fortune 500 financial services organization and one of the largest banking and credit card companies in the United States. The company combines financial services with technology, data, and digital innovation to deliver seamless experiences for millions of customers. With a strong focus on modernizing the banking experience, the organization operates in a fast-paced, highly collaborative environment where teams work cross-functionally to develop and deliver innovative products, services, and customer communications. Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance. Rate: $28.00 to $33.00/hr. w2 JN -26 Responsibilities: Partner with internal customers to develop and execute messaging based on defined intents and objectives. Build, test, and release digital messages for web and mobile banking applications within an established framework. Apply analytical thinking to create targeting that ensures messages reach the intended audience. Use Insomnia to perform GET and POST requests to test and troubleshoot messaging logic. Test messages across channels using web browsers, Xcode, and Android Studio. Communicate status updates to individuals and teams for assigned projects. Gather and analyze data from Tableau, Splunk, or other sources as requested. Follow established team processes and contribute to process improvement. Experience Requirements: 3+ years of digital messaging experience. 1+ years of experience using macOS. Client relationship management with a focus on building relationships and meeting customer needs. Growth mindset with the ability to learn from challenges. Detail-oriented with the focus required to manage complex campaigns with multiple variables and stakeholders. Ability to prioritize and manage multiple projects in a fast-paced environment. Effective verbal and written communication with customers, team members, and mid-level management in person and virtually. Resourceful approach to problem solving. Content management system experience, including navigating, entering text, uploading images, and selecting metadata tags. Experience using Google Workspace products. Hybrid schedule with physical attendance onsite 3 days per week in Richmond is required. No rate flexibility, possibility to extend with no guarantee, and no possibility to convert. Recruitment Transparency Notice Eliassen Group values transparency in our recruitment practices. Please be advised that Eliassen Group utilizes artificial intelligence (AI) tools as part of its initial application screening and hiring process. You may receive email and SMS notifications from the Eliassen Virtual Recruiting Team ( , ) inviting you to complete a brief voice screening as part of your application process. These tools assist our hiring teams in different ways, including but not limited to, assistance in reviewing application materials to help identify candidates whose qualifications most closely match the requirements of the position. All AI-assisted evaluations and responses are reviewed by human recruiters before any hiring decisions are made. The use of AI in our process is intended to support fairness, efficiency, and consistency, and Eliassen Group takes measures to prevent bias or discrimination in connection with its hiring practices. By proceeding, you acknowledge, agree, and consent to Eliassen Group's use of these tools, including AI tools, as part of the application and hiring process. Skills, experience, and other compensable factors will be considered when determining pay rate. The pay range provided in this posting reflects a W2 hourly rate; other employment options may be available that may result in pay outside of the provided range.W2 employees of Eliassen Group who are regularly scheduled to work 30 or more hours per week are eligible for the following benefits: medical (choice of 3 plans), dental, vision, pre-tax accounts, other voluntary benefits including life and disability insurance, 401(k) with match, and sick time if required by law in the worked-in state/locality.If anyone reaches out to you about an open position connected with Eliassen Group, please ensure that you are working directly with us by confirming the following: When you work with Eliassen Group, all email communication will come from an address, never Gmail, Yahoo, etc. Eliassen Group will never ask you for personal information (home address, bank account, or check routing number) until you have worked with someone clearly associated with Eliassen Group. If you have any indication of fraudulent activity, please contact . About Eliassen Group: Eliassen Group is a strategic consulting firm that helps organizations reach further and achieve more through our technology, business advisory, and life sciences solutions. For nearly 40 years, we have combined exceptional people, deep domain expertise, and intelligent capabilities to expand our clients' capacity and accelerate meaningful outcomes. We are driven by a purpose to positively impact the lives of our employees, clients, consultants, and the communities we serve. Eliassen is committed to building a diverse and inclusive team from a variety of backgrounds, perspectives, and skills. We are an Equal Opportunity and Affirmative Action Employer and all employment decisions are based on merit, performance, and business needs. Eliassen does not discriminate on the basis of race, color, gender identity or expression, sexual preference or orientation, sex (including pregnancy, childbirth, and related medical conditions), marital status, creed, religion, physical or mental disability, genetic information, military or veteran status, age, ancestry, national origin, citizenship status, prohibited criminal record inquiries of applicants and employees, or any other category protected by federal, state, or local laws. Don't miss out on our referral program! If we hire a candidate that you refer us to then you can be eligible for a $1,000 referral check!

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. The Senior Business Intelligence Analyst is a highly visible, hands-on individual contributor responsible for building and scaling AIM28's analytics, reporting, and value-realization measurement capabilities. Reporting to the AIM28 Finance Lead, this role will design and own end-to-end data models, ETL pipelines, dashboards, and automation solutions that transform complex, fragmented data into trusted insights for executives and program leaders. In addition, this role will support other strategic projects as required. The ideal candidate combines deep technical expertise in SQL, data modeling, BI visualization, automation, and AI-enabled analytics with strong business and finance acumen. Operating as the sole analytics builder within the AIM28 Finance team, this person will create the program's single source of truth for KPI, benefit, investment, and value-realization reporting while driving self-service analytics, reporting automation, and AI-powered insights across the AIM28 portfolio. The Senior Business Intelligence Analyst will lead the development of business intelligence, data visualization, analytics, and reporting automation for the AIM28 program and other strategic projects within U.S. Pharmaceutical Distribution (USPD). AIM28 is McKesson's AI-enabled transformation program, organized around a portfolio of Big Bets, each accountable for delivering measurable business outcomes. This role reports directly into the Sr. Director, Finance (AIM28). This role is the hands-on builder who turns Big Bet data into trusted, automated benefit and value-realization reporting for program leadership and senior executives. The source material varies: some data arrives raw and needs business logic before anyone can use it, some lives in manual files, and some is already visualized in dashboards the business teams built for themselves. You will architect an AI-ready analytics and reporting model that pairs traditional dashboards with AI capabilities such as automated narratives and conversational agents, so stakeholders can self-serve and get to insight faster. Where data is raw, you will establish the transformation logic that makes it consumable. Where reporting lives in manual files or scattered dashboards, you will integrate and standardize it into Big Bet and AIM28 program-level solutions. This is a highly visible individual contributor role. We are looking for a builder with deep SQL proficiency, strong knowledge of data design, and the visualization and storytelling skill to brief senior executives. Expect problems to show up half-defined; your job is to turn them into reliable numbers and clear answers. This role works under the direction of the AIM28 Finance Lead partnering with, Big Bet teams, the transformation office, and McKesson Technology to codify KPI definitions and benefit methodologies into governed data models. The goal is one version of the truth across the program. This is a pure individual-contributor role embedded in Finance, not a role inside a BI or data engineering organization. There is no bench of other BIEs, analytics engineers, or data engineers to hand design, build, or troubleshooting off to, you own the full stack yourself, end to end. Because the role sits in Finance rather than Technology, you should also be comfortable working fluently in financial concepts: ROI, forecasting, variance-to-plan, and business-case logic, gained through direct experience partnering with a business finance or FP&A team. What You'll Do Program & Big Bet reporting Design, build, and maintain the AIM28 program-level and Big Bet-level dashboards that track KPIs, benefits, investments, milestones, and value realization. These dashboards are the single source of truth for program performance. Develop executive-facing reporting for monthly Steering Committee meetings, business reviews, and leadership updates. Translate complex program data into insights leaders can act on. Reporting automation Automate recurring benefit and program reporting end to end. Replace manual, file-based processes with scheduled data flows that are validated and monitored. Find and implement automation opportunities across reporting workflows, including AI capabilities such as automated narratives and summaries that surface insights before anyone asks for them. Data engineering & transformation Build and maintain scalable ETL/ELT pipelines that extract, transform, and clean large, multi-dimensional datasets from multiple sources. These pipelines power the automated Big Bet and AIM28 program-level reporting. Ingest and integrate data from Big Bet teams into harmonized, consumable datasets and data models. Sources include raw operational data, manually maintained files, and dashboards the business built for itself. Establish the business logic that converts raw source data into certified reporting. Document the logic, lineage, and metric definitions well enough that someone else could audit or maintain the work. Design semantic models and curated datasets that people consume today through dashboards and self-service, and that AI agents can consume over time. KPI & benefit frameworks Partner with the AIM28 Finance lead to codify KPI definitions and benefit-realization methodologies into governed data models, so measurement stays consistent across Big Bets. Develop and monitor the KPIs that drive business decisions. Implement data-quality checks, reconciliation, and anomaly detection to protect trust in the numbers. Deep-dive analysis & insights Conduct deep-dive analyses of KPI movements, benefit variances, and program performance questions. Identify root causes and bring conclusions and recommendations to senior leadership. Answer ad hoc analytical questions from program leadership and Big Bet teams. When sources disagree, triangulate across them to validate data fidelity. Self-service & adoption Enable governed self-service analytics for program and Big Bet stakeholders, so fewer questions turn into ad hoc requests or one-off analyses. Own delivery end to end, from requirements and solution design through build, UAT, and adoption. Work with business stakeholders, data engineering, and IT to keep solutions compliant with enterprise data standards. Continuous improvement & influence Prioritize the reporting and automation backlog by business impact and effort. Keep stakeholders current on the roadmap and the trade-offs behind it. Set the standard for analytics practice (data integrity, documentation, versioning) across Big Bet teams, and coach business analysts who build their own reporting. What You Bring A builder's mindset. You take loosely defined problems, work out the business intent behind them, and deliver working solutions end to end: requirements, data model, pipeline, dashboard, adoption. Comfort with ambiguity and imperfect data. Raw extracts, manual spreadsheets, inconsistent definitions, and shifting requirements are the starting point here, not a blocker. Business acumen and financial literacy. You understand core finance concepts, ROI, forecasting, variance-to-plan, business-case logic well enough to hold your own in a finance conversation, not just translate what finance tells you. You have worked directly with a finance or FP&A team before, and you can sit with finance and business partners, understand benefit logic and business cases, and translate between data and business needs. Comfort as a standalone technical IC. This role sits in Finance, not in a BI or data engineering organization. There is no dedicated BIE team, data engineering bench, or technical manager to lean on for design review, code review, or troubleshooting, you are the technical function for AIM28 reporting. You are comfortable being the only builder in the room and owning decisions end to end. A KPI and automation track record. You have developed and monitored KPIs that drive business decisions, and you have built automated reporting systems that replaced manual processes. Executive presence. You have experience communicating and presenting analytical results to senior leadership, in writing and in person. Ownership and judgment. You validate your own outputs, make pragmatic build-versus-reuse trade-offs, and earn trust by being right about the numbers. Organization and prioritization. You manage multiple concurrent workstreams across Big Bets without letting deliverables or timelines slip. Curiosity about AI. You are interested in applying GenAI and agents to reporting, and ideally you have already experimented with them. Technical Skills Expert SQL: complex query development, window functions, and performance tuning on large, multi-dimensional datasets from multiple sources. You write queries that need little post-processing. Data modeling and design: . click apply for full job details

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. The Senior Business Intelligence Analyst is a highly visible, hands-on individual contributor responsible for building and scaling AIM28's analytics, reporting, and value-realization measurement capabilities. Reporting to the AIM28 Finance Lead, this role will design and own end-to-end data models, ETL pipelines, dashboards, and automation solutions that transform complex, fragmented data into trusted insights for executives and program leaders. In addition, this role will support other strategic projects as required. The ideal candidate combines deep technical expertise in SQL, data modeling, BI visualization, automation, and AI-enabled analytics with strong business and finance acumen. Operating as the sole analytics builder within the AIM28 Finance team, this person will create the program's single source of truth for KPI, benefit, investment, and value-realization reporting while driving self-service analytics, reporting automation, and AI-powered insights across the AIM28 portfolio. The Senior Business Intelligence Analyst will lead the development of business intelligence, data visualization, analytics, and reporting automation for the AIM28 program and other strategic projects within U.S. Pharmaceutical Distribution (USPD). AIM28 is McKesson's AI-enabled transformation program, organized around a portfolio of Big Bets, each accountable for delivering measurable business outcomes. This role reports directly into the Sr. Director, Finance (AIM28). This role is the hands-on builder who turns Big Bet data into trusted, automated benefit and value-realization reporting for program leadership and senior executives. The source material varies: some data arrives raw and needs business logic before anyone can use it, some lives in manual files, and some is already visualized in dashboards the business teams built for themselves. You will architect an AI-ready analytics and reporting model that pairs traditional dashboards with AI capabilities such as automated narratives and conversational agents, so stakeholders can self-serve and get to insight faster. Where data is raw, you will establish the transformation logic that makes it consumable. Where reporting lives in manual files or scattered dashboards, you will integrate and standardize it into Big Bet and AIM28 program-level solutions. This is a highly visible individual contributor role. We are looking for a builder with deep SQL proficiency, strong knowledge of data design, and the visualization and storytelling skill to brief senior executives. Expect problems to show up half-defined; your job is to turn them into reliable numbers and clear answers. This role works under the direction of the AIM28 Finance Lead partnering with, Big Bet teams, the transformation office, and McKesson Technology to codify KPI definitions and benefit methodologies into governed data models. The goal is one version of the truth across the program. This is a pure individual-contributor role embedded in Finance, not a role inside a BI or data engineering organization. There is no bench of other BIEs, analytics engineers, or data engineers to hand design, build, or troubleshooting off to, you own the full stack yourself, end to end. Because the role sits in Finance rather than Technology, you should also be comfortable working fluently in financial concepts: ROI, forecasting, variance-to-plan, and business-case logic, gained through direct experience partnering with a business finance or FP&A team. What You'll Do Program & Big Bet reporting Design, build, and maintain the AIM28 program-level and Big Bet-level dashboards that track KPIs, benefits, investments, milestones, and value realization. These dashboards are the single source of truth for program performance. Develop executive-facing reporting for monthly Steering Committee meetings, business reviews, and leadership updates. Translate complex program data into insights leaders can act on. Reporting automation Automate recurring benefit and program reporting end to end. Replace manual, file-based processes with scheduled data flows that are validated and monitored. Find and implement automation opportunities across reporting workflows, including AI capabilities such as automated narratives and summaries that surface insights before anyone asks for them. Data engineering & transformation Build and maintain scalable ETL/ELT pipelines that extract, transform, and clean large, multi-dimensional datasets from multiple sources. These pipelines power the automated Big Bet and AIM28 program-level reporting. Ingest and integrate data from Big Bet teams into harmonized, consumable datasets and data models. Sources include raw operational data, manually maintained files, and dashboards the business built for itself. Establish the business logic that converts raw source data into certified reporting. Document the logic, lineage, and metric definitions well enough that someone else could audit or maintain the work. Design semantic models and curated datasets that people consume today through dashboards and self-service, and that AI agents can consume over time. KPI & benefit frameworks Partner with the AIM28 Finance lead to codify KPI definitions and benefit-realization methodologies into governed data models, so measurement stays consistent across Big Bets. Develop and monitor the KPIs that drive business decisions. Implement data-quality checks, reconciliation, and anomaly detection to protect trust in the numbers. Deep-dive analysis & insights Conduct deep-dive analyses of KPI movements, benefit variances, and program performance questions. Identify root causes and bring conclusions and recommendations to senior leadership. Answer ad hoc analytical questions from program leadership and Big Bet teams. When sources disagree, triangulate across them to validate data fidelity. Self-service & adoption Enable governed self-service analytics for program and Big Bet stakeholders, so fewer questions turn into ad hoc requests or one-off analyses. Own delivery end to end, from requirements and solution design through build, UAT, and adoption. Work with business stakeholders, data engineering, and IT to keep solutions compliant with enterprise data standards. Continuous improvement & influence Prioritize the reporting and automation backlog by business impact and effort. Keep stakeholders current on the roadmap and the trade-offs behind it. Set the standard for analytics practice (data integrity, documentation, versioning) across Big Bet teams, and coach business analysts who build their own reporting. What You Bring A builder's mindset. You take loosely defined problems, work out the business intent behind them, and deliver working solutions end to end: requirements, data model, pipeline, dashboard, adoption. Comfort with ambiguity and imperfect data. Raw extracts, manual spreadsheets, inconsistent definitions, and shifting requirements are the starting point here, not a blocker. Business acumen and financial literacy. You understand core finance concepts, ROI, forecasting, variance-to-plan, business-case logic well enough to hold your own in a finance conversation, not just translate what finance tells you. You have worked directly with a finance or FP&A team before, and you can sit with finance and business partners, understand benefit logic and business cases, and translate between data and business needs. Comfort as a standalone technical IC. This role sits in Finance, not in a BI or data engineering organization. There is no dedicated BIE team, data engineering bench, or technical manager to lean on for design review, code review, or troubleshooting, you are the technical function for AIM28 reporting. You are comfortable being the only builder in the room and owning decisions end to end. A KPI and automation track record. You have developed and monitored KPIs that drive business decisions, and you have built automated reporting systems that replaced manual processes. Executive presence. You have experience communicating and presenting analytical results to senior leadership, in writing and in person. Ownership and judgment. You validate your own outputs, make pragmatic build-versus-reuse trade-offs, and earn trust by being right about the numbers. Organization and prioritization. You manage multiple concurrent workstreams across Big Bets without letting deliverables or timelines slip. Curiosity about AI. You are interested in applying GenAI and agents to reporting, and ideally you have already experimented with them. Technical Skills Expert SQL: complex query development, window functions, and performance tuning on large, multi-dimensional datasets from multiple sources. You write queries that need little post-processing. Data modeling and design: . click apply for full job details

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. The Senior Business Intelligence Analyst is a highly visible, hands-on individual contributor responsible for building and scaling AIM28's analytics, reporting, and value-realization measurement capabilities. Reporting to the AIM28 Finance Lead, this role will design and own end-to-end data models, ETL pipelines, dashboards, and automation solutions that transform complex, fragmented data into trusted insights for executives and program leaders. In addition, this role will support other strategic projects as required. The ideal candidate combines deep technical expertise in SQL, data modeling, BI visualization, automation, and AI-enabled analytics with strong business and finance acumen. Operating as the sole analytics builder within the AIM28 Finance team, this person will create the program's single source of truth for KPI, benefit, investment, and value-realization reporting while driving self-service analytics, reporting automation, and AI-powered insights across the AIM28 portfolio. The Senior Business Intelligence Analyst will lead the development of business intelligence, data visualization, analytics, and reporting automation for the AIM28 program and other strategic projects within U.S. Pharmaceutical Distribution (USPD). AIM28 is McKesson's AI-enabled transformation program, organized around a portfolio of Big Bets, each accountable for delivering measurable business outcomes. This role reports directly into the Sr. Director, Finance (AIM28). This role is the hands-on builder who turns Big Bet data into trusted, automated benefit and value-realization reporting for program leadership and senior executives. The source material varies: some data arrives raw and needs business logic before anyone can use it, some lives in manual files, and some is already visualized in dashboards the business teams built for themselves. You will architect an AI-ready analytics and reporting model that pairs traditional dashboards with AI capabilities such as automated narratives and conversational agents, so stakeholders can self-serve and get to insight faster. Where data is raw, you will establish the transformation logic that makes it consumable. Where reporting lives in manual files or scattered dashboards, you will integrate and standardize it into Big Bet and AIM28 program-level solutions. This is a highly visible individual contributor role. We are looking for a builder with deep SQL proficiency, strong knowledge of data design, and the visualization and storytelling skill to brief senior executives. Expect problems to show up half-defined; your job is to turn them into reliable numbers and clear answers. This role works under the direction of the AIM28 Finance Lead partnering with, Big Bet teams, the transformation office, and McKesson Technology to codify KPI definitions and benefit methodologies into governed data models. The goal is one version of the truth across the program. This is a pure individual-contributor role embedded in Finance, not a role inside a BI or data engineering organization. There is no bench of other BIEs, analytics engineers, or data engineers to hand design, build, or troubleshooting off to, you own the full stack yourself, end to end. Because the role sits in Finance rather than Technology, you should also be comfortable working fluently in financial concepts: ROI, forecasting, variance-to-plan, and business-case logic, gained through direct experience partnering with a business finance or FP&A team. What You'll Do Program & Big Bet reporting Design, build, and maintain the AIM28 program-level and Big Bet-level dashboards that track KPIs, benefits, investments, milestones, and value realization. These dashboards are the single source of truth for program performance. Develop executive-facing reporting for monthly Steering Committee meetings, business reviews, and leadership updates. Translate complex program data into insights leaders can act on. Reporting automation Automate recurring benefit and program reporting end to end. Replace manual, file-based processes with scheduled data flows that are validated and monitored. Find and implement automation opportunities across reporting workflows, including AI capabilities such as automated narratives and summaries that surface insights before anyone asks for them. Data engineering & transformation Build and maintain scalable ETL/ELT pipelines that extract, transform, and clean large, multi-dimensional datasets from multiple sources. These pipelines power the automated Big Bet and AIM28 program-level reporting. Ingest and integrate data from Big Bet teams into harmonized, consumable datasets and data models. Sources include raw operational data, manually maintained files, and dashboards the business built for itself. Establish the business logic that converts raw source data into certified reporting. Document the logic, lineage, and metric definitions well enough that someone else could audit or maintain the work. Design semantic models and curated datasets that people consume today through dashboards and self-service, and that AI agents can consume over time. KPI & benefit frameworks Partner with the AIM28 Finance lead to codify KPI definitions and benefit-realization methodologies into governed data models, so measurement stays consistent across Big Bets. Develop and monitor the KPIs that drive business decisions. Implement data-quality checks, reconciliation, and anomaly detection to protect trust in the numbers. Deep-dive analysis & insights Conduct deep-dive analyses of KPI movements, benefit variances, and program performance questions. Identify root causes and bring conclusions and recommendations to senior leadership. Answer ad hoc analytical questions from program leadership and Big Bet teams. When sources disagree, triangulate across them to validate data fidelity. Self-service & adoption Enable governed self-service analytics for program and Big Bet stakeholders, so fewer questions turn into ad hoc requests or one-off analyses. Own delivery end to end, from requirements and solution design through build, UAT, and adoption. Work with business stakeholders, data engineering, and IT to keep solutions compliant with enterprise data standards. Continuous improvement & influence Prioritize the reporting and automation backlog by business impact and effort. Keep stakeholders current on the roadmap and the trade-offs behind it. Set the standard for analytics practice (data integrity, documentation, versioning) across Big Bet teams, and coach business analysts who build their own reporting. What You Bring A builder's mindset. You take loosely defined problems, work out the business intent behind them, and deliver working solutions end to end: requirements, data model, pipeline, dashboard, adoption. Comfort with ambiguity and imperfect data. Raw extracts, manual spreadsheets, inconsistent definitions, and shifting requirements are the starting point here, not a blocker. Business acumen and financial literacy. You understand core finance concepts, ROI, forecasting, variance-to-plan, business-case logic well enough to hold your own in a finance conversation, not just translate what finance tells you. You have worked directly with a finance or FP&A team before, and you can sit with finance and business partners, understand benefit logic and business cases, and translate between data and business needs. Comfort as a standalone technical IC. This role sits in Finance, not in a BI or data engineering organization. There is no dedicated BIE team, data engineering bench, or technical manager to lean on for design review, code review, or troubleshooting, you are the technical function for AIM28 reporting. You are comfortable being the only builder in the room and owning decisions end to end. A KPI and automation track record. You have developed and monitored KPIs that drive business decisions, and you have built automated reporting systems that replaced manual processes. Executive presence. You have experience communicating and presenting analytical results to senior leadership, in writing and in person. Ownership and judgment. You validate your own outputs, make pragmatic build-versus-reuse trade-offs, and earn trust by being right about the numbers. Organization and prioritization. You manage multiple concurrent workstreams across Big Bets without letting deliverables or timelines slip. Curiosity about AI. You are interested in applying GenAI and agents to reporting, and ideally you have already experimented with them. Technical Skills Expert SQL: complex query development, window functions, and performance tuning on large, multi-dimensional datasets from multiple sources. You write queries that need little post-processing. . click apply for full job details