Genesis10 is currently seeking a Data Engineer for a contract-to-hire position with a Global Manufacturing Company located in Whitehall, MI. This is a 6+ month contract-to-hire opportunity. Compensation: $40.00 - $60.00 per hour, W2, based on qualifications Position Overview: In this role, the successful candidate will collaborate closely with data scientists, data analysts, and IT counterparts to design data pipelines for the AI and analytical needs of a large manufacturing enterprise. This position involves modeling, building, and optimizing complex, scalable ETLs and data pipelines to support on-premise and cloud-based data warehouses and data lakes using SQL and Azure technologies. Extensive interaction with functional and IT resources will be required, making effective communication skills essential. Key Responsibilities: Design, develop, and support scalable Data Lake, Lakehouse, Data Warehouse, and AI-enabled data platforms across cloud and hybrid environments Design and develop ETL/ELT pipelines using Azure Data Factory, Microsoft Fabric, or similar technologies Design scalable data models and integration patterns to support enterprise analytics and AI initiatives Establish event-driven, streaming, batch, and API-based integration patterns across enterprise systems Build and maintain data integration pipelines for plant, business unit, or enterprise applications Implement data validation, monitoring, quality controls, and data governance best practices Develop reusable semantic models, datasets, and data products that enable self-service analytics Optimize Power BI performance, data refresh processes, and security, including Row-Level Security (RLS) and workspace governance Collaborate with business stakeholders to define reporting requirements, KPIs, and analytics solutions Optimize SQL queries and data processing performance to ensure scalable and efficient data solutions Evaluate emerging data and AI technologies and recommend solutions that improve business processes and operational efficiency Primary Requirements: Bachelor's degree with 3+ years of experience OR a High school diploma with 6+ years of experience Experience writing Python Experience in Azure Synapse or Fabric (ETL/ELT) Experience using DevOps to streamline pipeline deployment Ability to communicate technical concepts to non-technical audiences Passionate about moving technology forward and enjoys working in a collaborative team environment ITAR Compliance required Only candidates available and ready to work directly as Genesis10 employees will be considered for this position. If you have the described qualifications and are interested in this exciting opportunity, please apply! Ranked a Top Staffing Firm in the U.S. by Staffing Industry Analysts for six consecutive years, Genesis10 puts thousands of consultants and employees to work across the United States every year in contract, contract-for-hire, and permanent placement roles. With more than 300 active clients, Genesis10 provides access to many of the Fortune 100 firms and a variety of mid-market organizations across the full spectrum of industry verticals. For contract roles, Genesis10 offers the benefits listed below. If this is a perm-placement opportunity, our recruiter can talk you through the unique benefits offered for that particular client. Benefits of Working with Genesis10: Access to hundreds of clients, most who have been working with Genesis10 for 5-20+ years The opportunity to have a career-home in Genesis10; many of our consultants have been working exclusively with Genesis10 for years Access to an experienced, caring recruiting team (more than 7 years of experience, on average) Behavioral Health Platform Medical, Dental, Vision Health Savings Account Voluntary Hospital Indemnity (Critical Illness & Accident) Voluntary Term Life Insurance 401K Sick Pay (for applicable states/municipalities) Commuter Benefits (Dallas, NYC, SF, and Illinois) For multiple years running, Genesis10 has been recognized as a Top Staffing Firm in the U.S., as a Best Company for Work-Life Balance, as a Best Company for Career Growth, for Diversity, and for Leadership, amongst others. To learn more and to view all of our available career opportunities, please visit us at our website. Genesis10 is an Equal Opportunity Employer. Candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
11/13/2026
Full time
Genesis10 is currently seeking a Data Engineer for a contract-to-hire position with a Global Manufacturing Company located in Whitehall, MI. This is a 6+ month contract-to-hire opportunity. Compensation: $40.00 - $60.00 per hour, W2, based on qualifications Position Overview: In this role, the successful candidate will collaborate closely with data scientists, data analysts, and IT counterparts to design data pipelines for the AI and analytical needs of a large manufacturing enterprise. This position involves modeling, building, and optimizing complex, scalable ETLs and data pipelines to support on-premise and cloud-based data warehouses and data lakes using SQL and Azure technologies. Extensive interaction with functional and IT resources will be required, making effective communication skills essential. Key Responsibilities: Design, develop, and support scalable Data Lake, Lakehouse, Data Warehouse, and AI-enabled data platforms across cloud and hybrid environments Design and develop ETL/ELT pipelines using Azure Data Factory, Microsoft Fabric, or similar technologies Design scalable data models and integration patterns to support enterprise analytics and AI initiatives Establish event-driven, streaming, batch, and API-based integration patterns across enterprise systems Build and maintain data integration pipelines for plant, business unit, or enterprise applications Implement data validation, monitoring, quality controls, and data governance best practices Develop reusable semantic models, datasets, and data products that enable self-service analytics Optimize Power BI performance, data refresh processes, and security, including Row-Level Security (RLS) and workspace governance Collaborate with business stakeholders to define reporting requirements, KPIs, and analytics solutions Optimize SQL queries and data processing performance to ensure scalable and efficient data solutions Evaluate emerging data and AI technologies and recommend solutions that improve business processes and operational efficiency Primary Requirements: Bachelor's degree with 3+ years of experience OR a High school diploma with 6+ years of experience Experience writing Python Experience in Azure Synapse or Fabric (ETL/ELT) Experience using DevOps to streamline pipeline deployment Ability to communicate technical concepts to non-technical audiences Passionate about moving technology forward and enjoys working in a collaborative team environment ITAR Compliance required Only candidates available and ready to work directly as Genesis10 employees will be considered for this position. If you have the described qualifications and are interested in this exciting opportunity, please apply! Ranked a Top Staffing Firm in the U.S. by Staffing Industry Analysts for six consecutive years, Genesis10 puts thousands of consultants and employees to work across the United States every year in contract, contract-for-hire, and permanent placement roles. With more than 300 active clients, Genesis10 provides access to many of the Fortune 100 firms and a variety of mid-market organizations across the full spectrum of industry verticals. For contract roles, Genesis10 offers the benefits listed below. If this is a perm-placement opportunity, our recruiter can talk you through the unique benefits offered for that particular client. Benefits of Working with Genesis10: Access to hundreds of clients, most who have been working with Genesis10 for 5-20+ years The opportunity to have a career-home in Genesis10; many of our consultants have been working exclusively with Genesis10 for years Access to an experienced, caring recruiting team (more than 7 years of experience, on average) Behavioral Health Platform Medical, Dental, Vision Health Savings Account Voluntary Hospital Indemnity (Critical Illness & Accident) Voluntary Term Life Insurance 401K Sick Pay (for applicable states/municipalities) Commuter Benefits (Dallas, NYC, SF, and Illinois) For multiple years running, Genesis10 has been recognized as a Top Staffing Firm in the U.S., as a Best Company for Work-Life Balance, as a Best Company for Career Growth, for Diversity, and for Leadership, amongst others. To learn more and to view all of our available career opportunities, please visit us at our website. Genesis10 is an Equal Opportunity Employer. Candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Pay Range: $50.00hr - $55.00hr Job Overview: As a Consulting Data Engineer, the successful candidate will architect, build, and optimize the next generation of real-time healthcare data platforms and event-driven architectures. The candidate will play a foundational role in moving clinical, operational, and financial data away from legacy batch processing toward high-throughput, near-zero-latency streaming pipelines. In this role, the candidate will champion modern data engineering practices, design highly scalable cloud-native pipelines, and mentor a growing team of engineers. The candidate will bridge the gap between complex electronic health records (EHR) and data consumers-enabling data scientists, software engineers, and clinical analysts to securely access, stream, and query compliant data at scale to improve patient outcomes and operational efficiency. Core Responsibilities: Architect Real-Time Pipelines: Design, implement, and monitor high-throughput, fault-tolerant streaming pipelines that ingest and process millions of concurrent healthcare data events (e.g., live telemetry, HL7 feeds, ADT messages). Scale Google Cloud Infrastructure: Build and maintain scalable, secure, and cost-effective cloud data infrastructure using Google Cloud healthcare-native tools and Infrastructure as Code (IaC). Enforce Healthcare Compliance & Security: Uphold strict data security protocols (HIPAA, HITRUST, SOC2) by building robust data lakehouse architectures that enforce PHI/PII masking, role-based access control (RBAC), and encryption at rest and in transit. Enable Self-Service Data & Interoperability: Build out streaming data ecosystems that support modern healthcare interoperability, managing real-time data catalogs and schema registries. Promote Code Excellence: Write modular, highly reusable, and performance-tested code; lead code reviews and establish standardized CI/CD automated deployment workflows. Optimize Data Reliability: Set and champion strict SLAs for clinical data delivery, establish automated data-quality testing, and implement proactive system monitoring and alerting. Collaborate & Mentor: Work closely with Data Architects, Clinical Product Managers, and AI/ML teams to translate healthcare requirements into technical blueprints while mentoring mid-level engineers. Required Qualifications: 13+ years of experience in dedicated data engineering, data architecture, or distributed systems software engineering roles. 4+ years of experience specifically within the healthcare domain (payer, provider, digital health, or health tech). Google Cloud Certified Professional Data Engineer (or equivalent proven expert-level architecture experience on GCP). Advanced Event-Driven Architecture: Mastery of high-volume streaming technologies including Apache Kafka and Google Cloud Pub/Sub for decoupled, real-time message routing. Stream Processing: Deep hands-on experience with Apache Flink or Google Cloud Dataflow (Apache Beam) for real-time streaming, complex event processing (CEP), windowing, and streaming data transformations. Interoperability Standards: Deep expertise working with FHIR (Fast Healthcare Interoperability Resources) JSON structures, HL7 v2/v3 messaging, and DICOM imaging standard streams. GCP Healthcare Tools: Hands-on experience implementing and scaling the Google Cloud Healthcare API (specifically FHIR stores and HL7v2 stores). EHR Integration: Experience integrating streaming pipelines with major EHR systems (such as Epic, Cerner, or Meditech). Google Cloud Ecosystem: Technical mastery of BigQuery (including partitioning, clustering, and streaming inserts), Cloud Storage, Dataproc, and Cloud Composer (Apache Airflow). Programming Languages: Proficiency in Python, Scala, or Java, with a strong focus on writing optimized code for distributed computing platforms (like Spark or Beam). Modern Data Stack & Modeling: Mastery of modern analytics engineering frameworks (such as dbt), dimensional data modeling, and lakehouse architectures (e.g., Iceberg, Delta Lake). DevOps & Automation: Robust experience building automated CI/CD pipelines (GitHub Actions, GitLab CI) and managing infrastructure using Terraform. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
11/13/2026
Full time
Pay Range: $50.00hr - $55.00hr Job Overview: As a Consulting Data Engineer, the successful candidate will architect, build, and optimize the next generation of real-time healthcare data platforms and event-driven architectures. The candidate will play a foundational role in moving clinical, operational, and financial data away from legacy batch processing toward high-throughput, near-zero-latency streaming pipelines. In this role, the candidate will champion modern data engineering practices, design highly scalable cloud-native pipelines, and mentor a growing team of engineers. The candidate will bridge the gap between complex electronic health records (EHR) and data consumers-enabling data scientists, software engineers, and clinical analysts to securely access, stream, and query compliant data at scale to improve patient outcomes and operational efficiency. Core Responsibilities: Architect Real-Time Pipelines: Design, implement, and monitor high-throughput, fault-tolerant streaming pipelines that ingest and process millions of concurrent healthcare data events (e.g., live telemetry, HL7 feeds, ADT messages). Scale Google Cloud Infrastructure: Build and maintain scalable, secure, and cost-effective cloud data infrastructure using Google Cloud healthcare-native tools and Infrastructure as Code (IaC). Enforce Healthcare Compliance & Security: Uphold strict data security protocols (HIPAA, HITRUST, SOC2) by building robust data lakehouse architectures that enforce PHI/PII masking, role-based access control (RBAC), and encryption at rest and in transit. Enable Self-Service Data & Interoperability: Build out streaming data ecosystems that support modern healthcare interoperability, managing real-time data catalogs and schema registries. Promote Code Excellence: Write modular, highly reusable, and performance-tested code; lead code reviews and establish standardized CI/CD automated deployment workflows. Optimize Data Reliability: Set and champion strict SLAs for clinical data delivery, establish automated data-quality testing, and implement proactive system monitoring and alerting. Collaborate & Mentor: Work closely with Data Architects, Clinical Product Managers, and AI/ML teams to translate healthcare requirements into technical blueprints while mentoring mid-level engineers. Required Qualifications: 13+ years of experience in dedicated data engineering, data architecture, or distributed systems software engineering roles. 4+ years of experience specifically within the healthcare domain (payer, provider, digital health, or health tech). Google Cloud Certified Professional Data Engineer (or equivalent proven expert-level architecture experience on GCP). Advanced Event-Driven Architecture: Mastery of high-volume streaming technologies including Apache Kafka and Google Cloud Pub/Sub for decoupled, real-time message routing. Stream Processing: Deep hands-on experience with Apache Flink or Google Cloud Dataflow (Apache Beam) for real-time streaming, complex event processing (CEP), windowing, and streaming data transformations. Interoperability Standards: Deep expertise working with FHIR (Fast Healthcare Interoperability Resources) JSON structures, HL7 v2/v3 messaging, and DICOM imaging standard streams. GCP Healthcare Tools: Hands-on experience implementing and scaling the Google Cloud Healthcare API (specifically FHIR stores and HL7v2 stores). EHR Integration: Experience integrating streaming pipelines with major EHR systems (such as Epic, Cerner, or Meditech). Google Cloud Ecosystem: Technical mastery of BigQuery (including partitioning, clustering, and streaming inserts), Cloud Storage, Dataproc, and Cloud Composer (Apache Airflow). Programming Languages: Proficiency in Python, Scala, or Java, with a strong focus on writing optimized code for distributed computing platforms (like Spark or Beam). Modern Data Stack & Modeling: Mastery of modern analytics engineering frameworks (such as dbt), dimensional data modeling, and lakehouse architectures (e.g., Iceberg, Delta Lake). DevOps & Automation: Robust experience building automated CI/CD pipelines (GitHub Actions, GitLab CI) and managing infrastructure using Terraform. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
This job is with Gilead Sciences Inc., an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. At Gilead, we're creating a healthier world for all people. For more than 35 years, we've tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer - working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world's biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference. Every member of Gilead's team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we're looking for the next wave of passionate and ambitious people ready to make a direct impact. We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together. Job Description As Associate Director, Data Science, you will proactively bring innovative data science techniques and insights to business to drive/support strategic business decisions. You would be responsible for standing up the capability of integrated HCP, patient journey and predictive sciences at scale for commercial market. The person will work closely with integrated insights, commercial ops, patient engagement and omni channel analytics leads. Hands on functional expertise, highly collaborative mindset and leadership skillsets required. This role will report to Sr. Director, Marketing Sciences and based in either Foster City, CA or Parsippany, NJ. Responsibilities: Be a partner in driving the industrialization of predictive sciences to help understand the patient journey and triggers robustly for markets. Understand Gilead's commercial business objectives, develop and deploy scalable data science products and insights to influence decisions in marketing, sales, medical and etc. Lead Data science projects end to end include convert unconstructured business questions into data science solutions, give guidance to offshore, be a hands-on leader who knows how to code and debug, communicate with stakeholders and etc. Foster a culture of measurement and impact and incorporate feedback to continuously improve data science models Bring thought leadership and thorough understanding of statistics, primarily predictive algorithms & methodologies, to construct robust propensity models for impactful commercial use Create data science products that can be refreshed, reproduced and replicated Work with other Data Scientists and Analysts to define retraining schedule and measure propensity models for impact Partner with global teams to cross-pollinate ideas and replicate successful models from other countries and vice versa Excellent communication and ability to abstract backend complexity where it is not needed Manage DS initiatives and deliveries for a priority TA Competencies: Results orientation. The ideal candidate will thrive in a fast-paced, high-growth environment. He/She/They will have a strong drive to meet and exceed goals, even under adverse circumstances, and is willing to take ownership of problems and make decisions in order to move forward. The candidate will be adept at clearly defining objectives and priorities and establishing appropriate milestones through a strong understanding of the market and innovative approaches. He/She/They will assign accountability and incorporate disciplined processes to ensure success and nimbly handle 'derailments' with contingency plans and coordination. Team leadership. The successful candidate will have demonstrated the ability to build, develop, and motivate a team as evidenced by effective hiring, coaching, and mentoring of direct reports. Additionally, this individual will have a track record of successfully leveraging diversity of thought to achieve the highest level of results. The ideal candidate will develop an inclusive culture comfortable with challenging current market paradigms to achieve and exceed goals. He/She/They will also have constructed an appropriate succession plan and process. Strategic Orientation. The successful candidate will be able to articulate evolving priorities for the business, identify market opportunities and adapt short-term plans. This includes the ability to strategically assess customer needs and build/execute specific, segmented strategies to win business across a diverse set of customers. The successful candidate will have passion for, and success in, developing commercial strategies that have measurable business impact. Collaboration and Influence. The qualified candidate must be able to influence without ownership, which they will do by demonstrating competence and confidence, being personally accessible and likable, being a good listener, use fact-based persuasion, passion, and persistence as appropriate to the audience and issue at stake. Gaining the support and involvement of key internal constituencies will be critical to success, as will influencing those important constituencies external to the company. The successful candidate must therefore have excellent skills at influencing outcomes and shaping and catalyzing dialogue across and outside the organization. Credibility and integrity are critical attributes to effectively command the respect and trust of key individuals Basic Qualifications: Bachelor's Degree and Eight Years' Experience OR Masters' Degree and Six Years' Experience OR PhD and Two Years' Experience Preferred Qualifications: Strong working knowledge of machine learning algorithms, including regression, clustering, neural networks, Bayesian models, RNN, CNN, Tree-based algorithms (RF, XGB, LightGBM), SMOTE and etc. Experience in building, implementing and using AI-based solutions with proven business impact Experience working with standard pharma and consumer data types and sources such as patient claims, Xponent, Plantrak, sales, activity. Strong leadership that be able to manage initiatives from beginning to end himself/herself Effective written and verbal communication skills Experience in implementing, optimizing and using AI-based solutions to establish proven business impact Expertise in commonly used pharma datasets such as IQVIA, Symphony, Komodo claims, Optum, Definitive health, Health Verity, EMR/HER Expertise in Python including commonly used data science libraries such as numpy, pandas, scikit-learn, seaborn, networkx, etc. Expertise in data science techniques such ANCOVA, Bayesian Statistics, Econometric modeling, Neural Networks/Logistic, etc. Understanding of cloud-based technologies and tools such as Databricks, S3, etc. Demonstrated product mindset Familiarity with product management principles Effective written and verbal communication skills Strong team player. Inclusive, objective, cross-functional, team member with a positive and solution-oriented mindset Understanding of emerging data science capabilities (fields, methodologies, algorithms, etc.) and potential application in pharma/health care Thorough understanding of datasets including their strengths and limitations such as capture rate, projections and acceptable error ranges for different therapeutic spaces Masters degree in a quantitative field with at least 8 years of relevant data science/analytics experience OR Undergraduate degree in a quantitative field with at least 10 years of relevant data science/analytics experience People Leader Accountabilities: Create Inclusion - knowing the business value of diverse teams, modeling inclusion, and embedding the value of diversity in the way they manage their teams. Develop Talent - understand the skills, experience, aspirations, and potential of their employees and coach them on current performance and future potential. They ensure employees are receiving the feedback and insight needed to grow, develop, and realize their purpose. Empower Teams - connect the team to the organization by aligning goals, purpose, and organizational objectives and holding them to account. They provide the support needed to remove barriers and connect their team to the broader ecosystem. The salary range for this position is: Bay Area: $195,670.00 - $253,220.00. Other US Locations: $177,905.00 - $230,230.00. Gilead considers a variety of factors when determining base compensation, including experience, qualifications, and geographic location. These considerations mean actual compensation will vary. This position may also be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives (eligibility may vary based on role), paid time off, and a benefits package. Benefits include company-sponsored medical, dental, vision, and life insurance plans . For additional benefits information, visit: Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans click apply for full job details
11/13/2026
Full time
This job is with Gilead Sciences Inc., an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. At Gilead, we're creating a healthier world for all people. For more than 35 years, we've tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer - working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world's biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference. Every member of Gilead's team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we're looking for the next wave of passionate and ambitious people ready to make a direct impact. We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together. Job Description As Associate Director, Data Science, you will proactively bring innovative data science techniques and insights to business to drive/support strategic business decisions. You would be responsible for standing up the capability of integrated HCP, patient journey and predictive sciences at scale for commercial market. The person will work closely with integrated insights, commercial ops, patient engagement and omni channel analytics leads. Hands on functional expertise, highly collaborative mindset and leadership skillsets required. This role will report to Sr. Director, Marketing Sciences and based in either Foster City, CA or Parsippany, NJ. Responsibilities: Be a partner in driving the industrialization of predictive sciences to help understand the patient journey and triggers robustly for markets. Understand Gilead's commercial business objectives, develop and deploy scalable data science products and insights to influence decisions in marketing, sales, medical and etc. Lead Data science projects end to end include convert unconstructured business questions into data science solutions, give guidance to offshore, be a hands-on leader who knows how to code and debug, communicate with stakeholders and etc. Foster a culture of measurement and impact and incorporate feedback to continuously improve data science models Bring thought leadership and thorough understanding of statistics, primarily predictive algorithms & methodologies, to construct robust propensity models for impactful commercial use Create data science products that can be refreshed, reproduced and replicated Work with other Data Scientists and Analysts to define retraining schedule and measure propensity models for impact Partner with global teams to cross-pollinate ideas and replicate successful models from other countries and vice versa Excellent communication and ability to abstract backend complexity where it is not needed Manage DS initiatives and deliveries for a priority TA Competencies: Results orientation. The ideal candidate will thrive in a fast-paced, high-growth environment. He/She/They will have a strong drive to meet and exceed goals, even under adverse circumstances, and is willing to take ownership of problems and make decisions in order to move forward. The candidate will be adept at clearly defining objectives and priorities and establishing appropriate milestones through a strong understanding of the market and innovative approaches. He/She/They will assign accountability and incorporate disciplined processes to ensure success and nimbly handle 'derailments' with contingency plans and coordination. Team leadership. The successful candidate will have demonstrated the ability to build, develop, and motivate a team as evidenced by effective hiring, coaching, and mentoring of direct reports. Additionally, this individual will have a track record of successfully leveraging diversity of thought to achieve the highest level of results. The ideal candidate will develop an inclusive culture comfortable with challenging current market paradigms to achieve and exceed goals. He/She/They will also have constructed an appropriate succession plan and process. Strategic Orientation. The successful candidate will be able to articulate evolving priorities for the business, identify market opportunities and adapt short-term plans. This includes the ability to strategically assess customer needs and build/execute specific, segmented strategies to win business across a diverse set of customers. The successful candidate will have passion for, and success in, developing commercial strategies that have measurable business impact. Collaboration and Influence. The qualified candidate must be able to influence without ownership, which they will do by demonstrating competence and confidence, being personally accessible and likable, being a good listener, use fact-based persuasion, passion, and persistence as appropriate to the audience and issue at stake. Gaining the support and involvement of key internal constituencies will be critical to success, as will influencing those important constituencies external to the company. The successful candidate must therefore have excellent skills at influencing outcomes and shaping and catalyzing dialogue across and outside the organization. Credibility and integrity are critical attributes to effectively command the respect and trust of key individuals Basic Qualifications: Bachelor's Degree and Eight Years' Experience OR Masters' Degree and Six Years' Experience OR PhD and Two Years' Experience Preferred Qualifications: Strong working knowledge of machine learning algorithms, including regression, clustering, neural networks, Bayesian models, RNN, CNN, Tree-based algorithms (RF, XGB, LightGBM), SMOTE and etc. Experience in building, implementing and using AI-based solutions with proven business impact Experience working with standard pharma and consumer data types and sources such as patient claims, Xponent, Plantrak, sales, activity. Strong leadership that be able to manage initiatives from beginning to end himself/herself Effective written and verbal communication skills Experience in implementing, optimizing and using AI-based solutions to establish proven business impact Expertise in commonly used pharma datasets such as IQVIA, Symphony, Komodo claims, Optum, Definitive health, Health Verity, EMR/HER Expertise in Python including commonly used data science libraries such as numpy, pandas, scikit-learn, seaborn, networkx, etc. Expertise in data science techniques such ANCOVA, Bayesian Statistics, Econometric modeling, Neural Networks/Logistic, etc. Understanding of cloud-based technologies and tools such as Databricks, S3, etc. Demonstrated product mindset Familiarity with product management principles Effective written and verbal communication skills Strong team player. Inclusive, objective, cross-functional, team member with a positive and solution-oriented mindset Understanding of emerging data science capabilities (fields, methodologies, algorithms, etc.) and potential application in pharma/health care Thorough understanding of datasets including their strengths and limitations such as capture rate, projections and acceptable error ranges for different therapeutic spaces Masters degree in a quantitative field with at least 8 years of relevant data science/analytics experience OR Undergraduate degree in a quantitative field with at least 10 years of relevant data science/analytics experience People Leader Accountabilities: Create Inclusion - knowing the business value of diverse teams, modeling inclusion, and embedding the value of diversity in the way they manage their teams. Develop Talent - understand the skills, experience, aspirations, and potential of their employees and coach them on current performance and future potential. They ensure employees are receiving the feedback and insight needed to grow, develop, and realize their purpose. Empower Teams - connect the team to the organization by aligning goals, purpose, and organizational objectives and holding them to account. They provide the support needed to remove barriers and connect their team to the broader ecosystem. The salary range for this position is: Bay Area: $195,670.00 - $253,220.00. Other US Locations: $177,905.00 - $230,230.00. Gilead considers a variety of factors when determining base compensation, including experience, qualifications, and geographic location. These considerations mean actual compensation will vary. This position may also be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives (eligibility may vary based on role), paid time off, and a benefits package. Benefits include company-sponsored medical, dental, vision, and life insurance plans . For additional benefits information, visit: Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans click apply for full job details
This job is with HP, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. AI Platforms Analytics and Governance Lead Description - Job Summary The rapid adoption of AI platforms such as GitHub Copilot, Azure OpenAI, and enterprise AI agents has created a need for specialized expertise to monitor usage patterns, optimize consumption, control costs, and maximize business value. Traditional Data Analyst roles primarily focus on reporting and dashboarding, while traditional Data Scientist roles focus on model development and machine learning. This position combines both disciplines to provide actionable insights, predictive analytics, and cost optimization strategies for enterprise AI investments. This will provide sustainable enterprise-scale AI adoption by balancing innovation, governance, cost management, and business value realization. This role ensures use of statistical and analytical methods to explore data, identify trends, patterns, and anomalies, and extract actionable insights including model analysis, predictive analytics, and cost optimization strategies for enterprise AI investments. The role works closely with teams across the organization to understand their data needs, provide insights, and support their data-related initiatives. Responsibilities Manage and optimize enterprise AI platforms including GitHub Copilot and OpenAI. Define platform standards, governance policies, and model consumption guidelines. Evaluate emerging AI technologies and recommend adoption strategies. Partner with security, legal, privacy, and compliance teams. Develop budget guardrails and cost allocation strategies. Create consumption forecasting models and executive dashboards. Identify optimization opportunities including: Prompt engineering Prompt caching Model routing Token reduction Context window optimization RAG optimization Batch processing Agent orchestration improvements Defines and creates extensive plans, data collection and analysis procedures and data insight visualizations for assigned projects. Analyzing AI platform usage, token consumption, licensing utilization, and user adoption trends. Developing forecasting models to predict AI credit consumption, budget requirements, and capacity needs. Leverages recognized domain expertise, business acumen, and experience to influence decisions of executive business leadership, development partners, and industry standards groups. Drives innovation and integration of new data science related technologies and practices into projects and activities in the business. Stays up-to-date with emerging technologies and trends in the business intelligence field, and recommends innovative solutions. Acts as a functional manager within area of expertise, developing strategy and setting functional policy and direction. Provides mentorship and guidance to lower-level employees, thus ensuring the realization of operational and strategic plans. Creating executive dashboards and KPIs to measure AI adoption, business value realization, and return on investment. Performing statistical analysis and machine learning techniques to detect anomalies, forecast spend, and recommend efficiency improvements. Partnering with platform owners, engineering teams, finance, procurement, and business stakeholders to drive data-driven decisions. Education & Experience Recommended Four-year or Graduate Degree in Mathematics, Statistics, Economics, Computer Science, or any other related discipline or commensurate work experience or demonstrated competence. Typically has 7+ years of work experience, preferably in data analytics, database management, statistical analysis, or a related field. Preferred Certifications Programming Language/s Certification (SQL, Python, or similar) Knowledge & Skills • Agile Methodology • Business Intelligence • Computer Science • Dashboard • Data Analysis • Data Management • Data Modeling • Data Quality • Data Science • Data Visualization • Data Warehousing • Extract Transform Load (ETL) • Machine Learning • Power BI • Python (Programming Language) • R (Programming Language) • SAS (Software) • SQL (Programming Language) • Statistics • Tableau (Business Intelligence Software) Cross-Org Skills • Effective Communication • Results Orientation • Learning Agility • Digital Fluency • Customer Centricity Impact & Scope Impacts large functions and leads large, cross-division functional teams or projects. Complexity Provides highly innovative solutions to complex problems within established policy. Pay & Benefits The pay range for this role is $130,700 to $205,200 USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience. Benefits: HP offers a comprehensive benefits package for this position, including: Health insurance Dental insurance Vision insurance Long term/short term disability insurance Employee assistance program Flexible spending account Life insurance Generous time off policies, including; 4-12 weeks fully paid parental leave based on tenure 11 paid holidays Additional flexible paid vacation and sick leave US benefits overview The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law. Disclaimer This job description describes the general nature and level of work performed in this role. It is not intended to be an exhaustive list of all duties, skills, responsibilities, knowledge, etc. These may be subject to change and additional functions may be assigned as needed by management. Job - Software Schedule - Full time Shift - No shift premium (United States of America) Travel - Relocation - Yes Equal Opportunity Employer (EEO) - HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s). Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence. For more information, review HP's EEO Policy or read about your rights as an applicant under the law here: " Know Your Rights: Workplace Discrimination is Illegal "
11/13/2026
Full time
This job is with HP, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. AI Platforms Analytics and Governance Lead Description - Job Summary The rapid adoption of AI platforms such as GitHub Copilot, Azure OpenAI, and enterprise AI agents has created a need for specialized expertise to monitor usage patterns, optimize consumption, control costs, and maximize business value. Traditional Data Analyst roles primarily focus on reporting and dashboarding, while traditional Data Scientist roles focus on model development and machine learning. This position combines both disciplines to provide actionable insights, predictive analytics, and cost optimization strategies for enterprise AI investments. This will provide sustainable enterprise-scale AI adoption by balancing innovation, governance, cost management, and business value realization. This role ensures use of statistical and analytical methods to explore data, identify trends, patterns, and anomalies, and extract actionable insights including model analysis, predictive analytics, and cost optimization strategies for enterprise AI investments. The role works closely with teams across the organization to understand their data needs, provide insights, and support their data-related initiatives. Responsibilities Manage and optimize enterprise AI platforms including GitHub Copilot and OpenAI. Define platform standards, governance policies, and model consumption guidelines. Evaluate emerging AI technologies and recommend adoption strategies. Partner with security, legal, privacy, and compliance teams. Develop budget guardrails and cost allocation strategies. Create consumption forecasting models and executive dashboards. Identify optimization opportunities including: Prompt engineering Prompt caching Model routing Token reduction Context window optimization RAG optimization Batch processing Agent orchestration improvements Defines and creates extensive plans, data collection and analysis procedures and data insight visualizations for assigned projects. Analyzing AI platform usage, token consumption, licensing utilization, and user adoption trends. Developing forecasting models to predict AI credit consumption, budget requirements, and capacity needs. Leverages recognized domain expertise, business acumen, and experience to influence decisions of executive business leadership, development partners, and industry standards groups. Drives innovation and integration of new data science related technologies and practices into projects and activities in the business. Stays up-to-date with emerging technologies and trends in the business intelligence field, and recommends innovative solutions. Acts as a functional manager within area of expertise, developing strategy and setting functional policy and direction. Provides mentorship and guidance to lower-level employees, thus ensuring the realization of operational and strategic plans. Creating executive dashboards and KPIs to measure AI adoption, business value realization, and return on investment. Performing statistical analysis and machine learning techniques to detect anomalies, forecast spend, and recommend efficiency improvements. Partnering with platform owners, engineering teams, finance, procurement, and business stakeholders to drive data-driven decisions. Education & Experience Recommended Four-year or Graduate Degree in Mathematics, Statistics, Economics, Computer Science, or any other related discipline or commensurate work experience or demonstrated competence. Typically has 7+ years of work experience, preferably in data analytics, database management, statistical analysis, or a related field. Preferred Certifications Programming Language/s Certification (SQL, Python, or similar) Knowledge & Skills • Agile Methodology • Business Intelligence • Computer Science • Dashboard • Data Analysis • Data Management • Data Modeling • Data Quality • Data Science • Data Visualization • Data Warehousing • Extract Transform Load (ETL) • Machine Learning • Power BI • Python (Programming Language) • R (Programming Language) • SAS (Software) • SQL (Programming Language) • Statistics • Tableau (Business Intelligence Software) Cross-Org Skills • Effective Communication • Results Orientation • Learning Agility • Digital Fluency • Customer Centricity Impact & Scope Impacts large functions and leads large, cross-division functional teams or projects. Complexity Provides highly innovative solutions to complex problems within established policy. Pay & Benefits The pay range for this role is $130,700 to $205,200 USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience. Benefits: HP offers a comprehensive benefits package for this position, including: Health insurance Dental insurance Vision insurance Long term/short term disability insurance Employee assistance program Flexible spending account Life insurance Generous time off policies, including; 4-12 weeks fully paid parental leave based on tenure 11 paid holidays Additional flexible paid vacation and sick leave US benefits overview The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law. Disclaimer This job description describes the general nature and level of work performed in this role. It is not intended to be an exhaustive list of all duties, skills, responsibilities, knowledge, etc. These may be subject to change and additional functions may be assigned as needed by management. Job - Software Schedule - Full time Shift - No shift premium (United States of America) Travel - Relocation - Yes Equal Opportunity Employer (EEO) - HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s). Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence. For more information, review HP's EEO Policy or read about your rights as an applicant under the law here: " Know Your Rights: Workplace Discrimination is Illegal "
About Us Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale - tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters - to you, to your community, and to the world. Progress starts with you. Job Description Overview: Visa Acceptance Intelligence is the foundation for innovative payment experiences through secure, resilient services and a broad ecosystem of technology and payments partners. We are building on that foundation to help businesses make smarter decisions with data. At Visa, we believe AI can transform how commerce works. Through Acceptance Analytics and Intelligence and Visa Intelligent Commerce, we're developing data products that connect information and surface actionable insights for Visa clients. We are looking for a curious, versatile Senior Applied Scientist to help advance this work. You'll partner with scientists, engineers, product leaders, and business stakeholders to design, build, and evaluate intelligent products-from forecasting and NLP solutions to carefully evaluate applications of large language models (LLMs). You will help turn promising ideas into reliable, useful capabilities for customers. If you enjoy working across research and production, questioning assumptions, and solving meaningful problems at scale, we'd like to meet you. Essential Functions: Develop and evaluate time-series analysis and forecasting approaches for business and payments use cases. Apply NLP and LLM techniques to data and intelligence products, including fine-tuning or other model-adaptation methods when appropriate. Design rigorous evaluation strategies for LLM-powered systems, including task-specific quality measures and assessment of reliability. Explore how LLMs can complement established analytical methods in time-series workflows, selecting techniques based on the problem, data, and evaluation results. Improve data extraction and preparation through data wrangling, complex data modeling, and AI-assisted approaches; raise the quality, completeness, and usability of data. Follow and strengthen data-management and governance practices that support high-quality, dependable products. Translate sophisticated technical and business needs into clear specifications with product owners, business partners, cybersecurity teams, and other collaborators. Identify dependencies across upstream and downstream systems and help shape solutions that work across the platform. Find meaningful patterns across relevant data sources and turn them into insights, product opportunities, and recommendations for future platform improvements. Contribute to technical roadmaps and, where relevant, recommend software upgrades or security remediation. Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager. Qualifications Basic Qualifications: 8+ years of relevant work experience with a Bachelor's Degree or at least 5 years of experience with an Advanced Degree (e.g., Master's, MBA, JD, MD) or 2 years of work experience with a PhD, OR 11+ years of relevant work experience. Preferred Qualifications: 9 or more years of relevant work experience with a Bachelor's Degree or 7 or more relevant years of experience with an Advanced Degree (e.g., Master's, MBA, JD, MD) or 3 or more years of experience with a PhD. PhD degree in computer science or relevant area. Experience in solving data problems using data technologies (e.g., Hadoop, Hive, Kafka, Redis, NoSQL, RDBMS). Experience with Big Data and Analytics in general leveraging technologies like Hadoop, Spark, Flink and MapReduce. Information for US Applicants For roles located in the US, the estimated salary range for this position is $162,500.00 to $ 260,400.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program. Work Hours Varies upon the needs of the department. Travel Requirements This position requires travel 5-10% of the time. Mental/Physical Requirements This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers. Visa is an EEO Employer Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.
11/13/2026
Full time
About Us Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale - tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters - to you, to your community, and to the world. Progress starts with you. Job Description Overview: Visa Acceptance Intelligence is the foundation for innovative payment experiences through secure, resilient services and a broad ecosystem of technology and payments partners. We are building on that foundation to help businesses make smarter decisions with data. At Visa, we believe AI can transform how commerce works. Through Acceptance Analytics and Intelligence and Visa Intelligent Commerce, we're developing data products that connect information and surface actionable insights for Visa clients. We are looking for a curious, versatile Senior Applied Scientist to help advance this work. You'll partner with scientists, engineers, product leaders, and business stakeholders to design, build, and evaluate intelligent products-from forecasting and NLP solutions to carefully evaluate applications of large language models (LLMs). You will help turn promising ideas into reliable, useful capabilities for customers. If you enjoy working across research and production, questioning assumptions, and solving meaningful problems at scale, we'd like to meet you. Essential Functions: Develop and evaluate time-series analysis and forecasting approaches for business and payments use cases. Apply NLP and LLM techniques to data and intelligence products, including fine-tuning or other model-adaptation methods when appropriate. Design rigorous evaluation strategies for LLM-powered systems, including task-specific quality measures and assessment of reliability. Explore how LLMs can complement established analytical methods in time-series workflows, selecting techniques based on the problem, data, and evaluation results. Improve data extraction and preparation through data wrangling, complex data modeling, and AI-assisted approaches; raise the quality, completeness, and usability of data. Follow and strengthen data-management and governance practices that support high-quality, dependable products. Translate sophisticated technical and business needs into clear specifications with product owners, business partners, cybersecurity teams, and other collaborators. Identify dependencies across upstream and downstream systems and help shape solutions that work across the platform. Find meaningful patterns across relevant data sources and turn them into insights, product opportunities, and recommendations for future platform improvements. Contribute to technical roadmaps and, where relevant, recommend software upgrades or security remediation. Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager. Qualifications Basic Qualifications: 8+ years of relevant work experience with a Bachelor's Degree or at least 5 years of experience with an Advanced Degree (e.g., Master's, MBA, JD, MD) or 2 years of work experience with a PhD, OR 11+ years of relevant work experience. Preferred Qualifications: 9 or more years of relevant work experience with a Bachelor's Degree or 7 or more relevant years of experience with an Advanced Degree (e.g., Master's, MBA, JD, MD) or 3 or more years of experience with a PhD. PhD degree in computer science or relevant area. Experience in solving data problems using data technologies (e.g., Hadoop, Hive, Kafka, Redis, NoSQL, RDBMS). Experience with Big Data and Analytics in general leveraging technologies like Hadoop, Spark, Flink and MapReduce. Information for US Applicants For roles located in the US, the estimated salary range for this position is $162,500.00 to $ 260,400.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program. Work Hours Varies upon the needs of the department. Travel Requirements This position requires travel 5-10% of the time. Mental/Physical Requirements This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers. Visa is an EEO Employer Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.
Job Description Job Description Description: Schumacher Homes is an award-winning company which strives to be the best on your lot custom homebuilder providing the ultimate customer experience. Quite simply, no one builds a better home or offers a better place to work. Apply today to join our outstanding team in our Canton, OH office (2715 Wise Ave NW, Canton, OH 44708). The Associate Data Scientist applies data science to marketing, reporting directly to the Head of Data Science. The role covers marketing activity and attribution, the customer journey from first contact to signed contract, and demographic and residential real estate data across the company's markets. The work informs decision-making across the organization, in partnership with marketing, sales, finance and executive leadership. Our Associate Data Scientist will : Support the Head of Data Science across key initiatives, including marketing analytics and attribution, customer journey mapping, statistical modeling, and demographic data and market analysis. Analyze data to identify trends, generate insights, and support data-driven business decisions. Collaborate on developing and improving analytical tools, models, and processes. AI Proficiency Integrate AI tools into daily workflows to improve efficiency, accelerate analysis, and support development. Use AI coding assistants to write, test, and refactor Python, SQL, and R. Leverage AI to quickly understand unfamiliar datasets, codebases, and technical documentation. Build lightweight, end-to-end solutions using AI, including applications, data pipelines, and API integrations. Validate AI-generated queries, data joins, calculations, and statistical results against source data to ensure accuracy before sharing findings. Benefits : Medical, dental, vision, life, critical illness and accident insurance 401K Plan + employer match Paid Time Off and Paid Holidays Homebuilding discount WORK HARD, HAVE FUN, PRODUCE RESULTS Requirements: Bachelor's degree in Statistics, Data Science, Mathematics, Economics, Computer Science, Geography/GIS, or a related quantitative field. 2+ years of hands-on data science experience, including graduate research, assistantships, or internships. Recent graduates are encouraged to apply. Strong foundation in statistical methods, including regression, classification, clustering, experimental design, and causal reasoning. Proficiency in Python and SQL; experience with R is a plus. Experience working with large, complex, and unstructured datasets. Demonstrated proficiency in using AI tools to support coding, data analysis, and solution development. Strong written and verbal communication skills, with the ability to explain technical findings to non-technical audiences. Preferred Requirements: Master's or other graduate degree in a quantitative field; PhD is a plus. Experience analyzing census data, public land records, or property data to inform business decisions. Familiarity with marketing analytics, including attribution, funnel analysis, cohort analysis, and propensity modeling. Experience working with CRM platforms such as Salesforce or HubSpot and cloud data tools such as Snowflake and dbt. Experience building data applications using Python frameworks such as Streamlit. You'll be at home with Schumacher Homes, the premier on-your-lot builder! Apply today. As part of our hiring process, we may use technology, including AI-assisted tools, to help review applications and support recruiting decisions. All hiring decisions are ultimately made by our recruiting team and hiring managers.
11/12/2026
Full time
Job Description Job Description Description: Schumacher Homes is an award-winning company which strives to be the best on your lot custom homebuilder providing the ultimate customer experience. Quite simply, no one builds a better home or offers a better place to work. Apply today to join our outstanding team in our Canton, OH office (2715 Wise Ave NW, Canton, OH 44708). The Associate Data Scientist applies data science to marketing, reporting directly to the Head of Data Science. The role covers marketing activity and attribution, the customer journey from first contact to signed contract, and demographic and residential real estate data across the company's markets. The work informs decision-making across the organization, in partnership with marketing, sales, finance and executive leadership. Our Associate Data Scientist will : Support the Head of Data Science across key initiatives, including marketing analytics and attribution, customer journey mapping, statistical modeling, and demographic data and market analysis. Analyze data to identify trends, generate insights, and support data-driven business decisions. Collaborate on developing and improving analytical tools, models, and processes. AI Proficiency Integrate AI tools into daily workflows to improve efficiency, accelerate analysis, and support development. Use AI coding assistants to write, test, and refactor Python, SQL, and R. Leverage AI to quickly understand unfamiliar datasets, codebases, and technical documentation. Build lightweight, end-to-end solutions using AI, including applications, data pipelines, and API integrations. Validate AI-generated queries, data joins, calculations, and statistical results against source data to ensure accuracy before sharing findings. Benefits : Medical, dental, vision, life, critical illness and accident insurance 401K Plan + employer match Paid Time Off and Paid Holidays Homebuilding discount WORK HARD, HAVE FUN, PRODUCE RESULTS Requirements: Bachelor's degree in Statistics, Data Science, Mathematics, Economics, Computer Science, Geography/GIS, or a related quantitative field. 2+ years of hands-on data science experience, including graduate research, assistantships, or internships. Recent graduates are encouraged to apply. Strong foundation in statistical methods, including regression, classification, clustering, experimental design, and causal reasoning. Proficiency in Python and SQL; experience with R is a plus. Experience working with large, complex, and unstructured datasets. Demonstrated proficiency in using AI tools to support coding, data analysis, and solution development. Strong written and verbal communication skills, with the ability to explain technical findings to non-technical audiences. Preferred Requirements: Master's or other graduate degree in a quantitative field; PhD is a plus. Experience analyzing census data, public land records, or property data to inform business decisions. Familiarity with marketing analytics, including attribution, funnel analysis, cohort analysis, and propensity modeling. Experience working with CRM platforms such as Salesforce or HubSpot and cloud data tools such as Snowflake and dbt. Experience building data applications using Python frameworks such as Streamlit. You'll be at home with Schumacher Homes, the premier on-your-lot builder! Apply today. As part of our hiring process, we may use technology, including AI-assisted tools, to help review applications and support recruiting decisions. All hiring decisions are ultimately made by our recruiting team and hiring managers.
Job Description Job Description Pay: $48.56 - $77.69 hourly, depending on experience Benefits: Medical, Dental, Vision, Paid Time Off, Paid Holidays, Employee Assistance Program, and other company-sponsored benefits Security Clearance: Ability to obtain and maintain a government security clearance if required Are you an experienced data scientist who enjoys solving complex problems, building advanced analytical models, and turning large datasets into actionable business intelligence? PEMCCO is seeking a Senior Data Scientist to lead data-driven initiatives that support critical government and mission-focused operations. This role is ideal for a highly skilled analytical professional who enjoys leveraging machine learning, predictive analytics, data visualization, and statistical modeling to influence strategic decisions and drive operational success. This opportunity is a strong fit for candidates with experience in data science, machine learning, predictive analytics, business intelligence, statistical analysis, operations research, artificial intelligence, advanced analytics, or data engineering. About PEMCCO PEMCCO supports complex technical and operational programs by providing skilled professionals who help customers achieve mission success. We offer opportunities to work on impactful projects, collaborate with experienced teams, and develop innovative solutions that support critical government operations. Job Overview As a Senior Data Scientist, you will apply advanced analytical, statistical, and computational methodologies to solve complex business and mission challenges. You will lead analytical initiatives, develop predictive and prescriptive models, and transform large, complex datasets into meaningful insights that support strategic planning, operational effectiveness, and informed decision-making. This position offers the opportunity to work with diverse data sources, advanced analytics tools, and cross-functional teams while helping organizations maximize the value of their data assets. What You'll Do Collect, analyze, interpret, and validate structured and unstructured data from multiple sources Develop and implement advanced statistical, predictive, and machine learning models Design and maintain data pipelines, analytical processes, and data workflows Create dashboards, reports, visualizations, and briefings that effectively communicate analytical findings Identify trends, patterns, anomalies, and opportunities for operational improvement Support data management, governance, and data quality initiatives Collaborate with engineers, analysts, and program personnel to define data requirements and develop analytical solutions Evaluate and recommend data science tools, technologies, and methodologies Present analytical findings, recommendations, and insights to leadership and stakeholders Provide technical guidance and mentorship to junior analysts and data scientists Document methodologies, assumptions, analytical processes, and findings Support mission, program, and organizational objectives through data-driven decision-making What You Bring Required Qualifications Master's degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, Information Systems, Operations Research, or a related quantitative field Ten (10) years of experience in data science, data analytics, statistical analysis, machine learning, predictive analytics, or a related field Strong knowledge of data science, statistical analysis, and predictive modeling techniques Knowledge of machine learning concepts and analytical methodologies Understanding of data management and data governance principles Ability to analyze and interpret large, complex datasets Ability to develop and communicate data-driven recommendations Strong analytical, critical-thinking, and problem-solving skills Strong written and verbal communication skills Ability to communicate effectively with both technical and non-technical audiences Proficiency with SQL and data analysis tools Ability to work independently and collaboratively within cross-functional teams Ability to obtain and maintain a government security clearance if required Preferred Experience Doctorate degree in Data Science, Statistics, Computer Science, Mathematics, Operations Research, or a related field Experience supporting Department of Defense or Federal Government programs Experience developing machine learning and predictive analytics solutions Experience with data visualization and business intelligence platforms Experience working within cloud-based data environments Experience leading analytical projects, research initiatives, or data-driven programs Why Join PEMCCO? Support critical government and customer missions through advanced analytics and data-driven decision-making Lead meaningful projects that influence operational and strategic outcomes Work with modern analytics, machine learning, and business intelligence technologies Collaborate with experienced technical professionals across multiple disciplines Solve complex problems using innovative analytical approaches Mentor and support the development of emerging data professionals Build a rewarding career in a collaborative and mission-focused environment Work Environment Professional office and technical work environments Work may be performed at company facilities, customer locations, or designated project sites as needed Regular use of computers, analytics platforms, visualization tools, and communication systems Frequent collaboration with technical teams, leadership, customers, and stakeholders Benefits Medical Insurance Dental Insurance Vision Insurance Paid Time Off Paid Holidays Employee Assistance Program Additional company-sponsored benefits Apply Today If you're an experienced data scientist looking to solve complex challenges, develop innovative analytical solutions, and help organizations make smarter, data-driven decisions, we encourage you to apply. Join PEMCCO and help transform data into actionable intelligence that drives mission success. Equal Opportunity Employer PEMCCO, Inc. is an equal opportunity employer. The Company does not discriminate on the basis of race, color, sex, sexual orientation, gender identity or expression, religion, national origin, age, disability, genetic information, military or veteran status, pregnancy, childbirth and related medical conditions, or any other characteristic protected by applicable federal, state, or local law.
11/12/2026
Full time
Job Description Job Description Pay: $48.56 - $77.69 hourly, depending on experience Benefits: Medical, Dental, Vision, Paid Time Off, Paid Holidays, Employee Assistance Program, and other company-sponsored benefits Security Clearance: Ability to obtain and maintain a government security clearance if required Are you an experienced data scientist who enjoys solving complex problems, building advanced analytical models, and turning large datasets into actionable business intelligence? PEMCCO is seeking a Senior Data Scientist to lead data-driven initiatives that support critical government and mission-focused operations. This role is ideal for a highly skilled analytical professional who enjoys leveraging machine learning, predictive analytics, data visualization, and statistical modeling to influence strategic decisions and drive operational success. This opportunity is a strong fit for candidates with experience in data science, machine learning, predictive analytics, business intelligence, statistical analysis, operations research, artificial intelligence, advanced analytics, or data engineering. About PEMCCO PEMCCO supports complex technical and operational programs by providing skilled professionals who help customers achieve mission success. We offer opportunities to work on impactful projects, collaborate with experienced teams, and develop innovative solutions that support critical government operations. Job Overview As a Senior Data Scientist, you will apply advanced analytical, statistical, and computational methodologies to solve complex business and mission challenges. You will lead analytical initiatives, develop predictive and prescriptive models, and transform large, complex datasets into meaningful insights that support strategic planning, operational effectiveness, and informed decision-making. This position offers the opportunity to work with diverse data sources, advanced analytics tools, and cross-functional teams while helping organizations maximize the value of their data assets. What You'll Do Collect, analyze, interpret, and validate structured and unstructured data from multiple sources Develop and implement advanced statistical, predictive, and machine learning models Design and maintain data pipelines, analytical processes, and data workflows Create dashboards, reports, visualizations, and briefings that effectively communicate analytical findings Identify trends, patterns, anomalies, and opportunities for operational improvement Support data management, governance, and data quality initiatives Collaborate with engineers, analysts, and program personnel to define data requirements and develop analytical solutions Evaluate and recommend data science tools, technologies, and methodologies Present analytical findings, recommendations, and insights to leadership and stakeholders Provide technical guidance and mentorship to junior analysts and data scientists Document methodologies, assumptions, analytical processes, and findings Support mission, program, and organizational objectives through data-driven decision-making What You Bring Required Qualifications Master's degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, Information Systems, Operations Research, or a related quantitative field Ten (10) years of experience in data science, data analytics, statistical analysis, machine learning, predictive analytics, or a related field Strong knowledge of data science, statistical analysis, and predictive modeling techniques Knowledge of machine learning concepts and analytical methodologies Understanding of data management and data governance principles Ability to analyze and interpret large, complex datasets Ability to develop and communicate data-driven recommendations Strong analytical, critical-thinking, and problem-solving skills Strong written and verbal communication skills Ability to communicate effectively with both technical and non-technical audiences Proficiency with SQL and data analysis tools Ability to work independently and collaboratively within cross-functional teams Ability to obtain and maintain a government security clearance if required Preferred Experience Doctorate degree in Data Science, Statistics, Computer Science, Mathematics, Operations Research, or a related field Experience supporting Department of Defense or Federal Government programs Experience developing machine learning and predictive analytics solutions Experience with data visualization and business intelligence platforms Experience working within cloud-based data environments Experience leading analytical projects, research initiatives, or data-driven programs Why Join PEMCCO? Support critical government and customer missions through advanced analytics and data-driven decision-making Lead meaningful projects that influence operational and strategic outcomes Work with modern analytics, machine learning, and business intelligence technologies Collaborate with experienced technical professionals across multiple disciplines Solve complex problems using innovative analytical approaches Mentor and support the development of emerging data professionals Build a rewarding career in a collaborative and mission-focused environment Work Environment Professional office and technical work environments Work may be performed at company facilities, customer locations, or designated project sites as needed Regular use of computers, analytics platforms, visualization tools, and communication systems Frequent collaboration with technical teams, leadership, customers, and stakeholders Benefits Medical Insurance Dental Insurance Vision Insurance Paid Time Off Paid Holidays Employee Assistance Program Additional company-sponsored benefits Apply Today If you're an experienced data scientist looking to solve complex challenges, develop innovative analytical solutions, and help organizations make smarter, data-driven decisions, we encourage you to apply. Join PEMCCO and help transform data into actionable intelligence that drives mission success. Equal Opportunity Employer PEMCCO, Inc. is an equal opportunity employer. The Company does not discriminate on the basis of race, color, sex, sexual orientation, gender identity or expression, religion, national origin, age, disability, genetic information, military or veteran status, pregnancy, childbirth and related medical conditions, or any other characteristic protected by applicable federal, state, or local law.
Starbucks is seeking a Senior/Lead Decision Scientist to shape data-driven strategy across its global coffeehouse business. In this role, you will partner with Retail, Supply Chain, and Marketing teams to design experiments, build predictive and optimization models, and translate complex data into clear, actionable insights. You will lead advanced analytics projects, define key metrics, and develop dashboards that guide pricing, assortment, and customer experience decisions. You will mentor other analysts, champion best practices in experimentation and data ethics, and help embed a culture of evidence-based decision making across Starbucks. Responsibilities Lead design and analysis of experiments to optimize pricing, promotions, and customer experience Build and deploy predictive and optimization models to support retail and supply chain decisions Develop dashboards and self-serve analytics tools for stakeholders Define and refine key performance metrics and measurement frameworks Translate complex analytical findings into clear, actionable business recommendations Partner with cross-functional teams in Marketing, Operations, and Supply Chain Mentor and guide junior analysts and decision scientists Promote best practices in data quality, experimentation, and analytics governance Required Skills Statistical modeling A/B testing and experimentation design Machine learning SQLPython or RData visualization (Tableau/Power BI) Predictive analytics Optimization and forecasting Big data tools (e.g., Spark, Hive) Business intelligence and dashboarding
11/01/2026
Full time
Starbucks is seeking a Senior/Lead Decision Scientist to shape data-driven strategy across its global coffeehouse business. In this role, you will partner with Retail, Supply Chain, and Marketing teams to design experiments, build predictive and optimization models, and translate complex data into clear, actionable insights. You will lead advanced analytics projects, define key metrics, and develop dashboards that guide pricing, assortment, and customer experience decisions. You will mentor other analysts, champion best practices in experimentation and data ethics, and help embed a culture of evidence-based decision making across Starbucks. Responsibilities Lead design and analysis of experiments to optimize pricing, promotions, and customer experience Build and deploy predictive and optimization models to support retail and supply chain decisions Develop dashboards and self-serve analytics tools for stakeholders Define and refine key performance metrics and measurement frameworks Translate complex analytical findings into clear, actionable business recommendations Partner with cross-functional teams in Marketing, Operations, and Supply Chain Mentor and guide junior analysts and decision scientists Promote best practices in data quality, experimentation, and analytics governance Required Skills Statistical modeling A/B testing and experimentation design Machine learning SQLPython or RData visualization (Tableau/Power BI) Predictive analytics Optimization and forecasting Big data tools (e.g., Spark, Hive) Business intelligence and dashboarding
Starbucks is seeking a Senior/Lead Decision Scientist to drive data-informed strategy across our global coffeehouse business. In this role, you'll translate complex data into clear insights that improve customer experience, store operations, and digital engagement. You'll design experiments, develop predictive and prescriptive models, and build dashboards that guide leaders in merchandising, pricing, and loyalty initiatives. Partnering with cross-functional teams, you'll define metrics, validate hypotheses, and tell compelling data stories. You'll mentor junior analysts, champion data quality and governance, and help shape a culture of evidence-based, ethical decision-making at scale. Responsibilities Lead design and execution of experiments and A/B tests to optimize customer experience, pricing, and promotions. Develop predictive and prescriptive models to support demand forecasting, personalization, and store operations. Translate complex data into clear, actionable insights and narratives for business and executive stakeholders. Build and maintain dashboards, reports, and self-service analytics tools for cross-functional teams. Define and refine key performance metrics for digital, retail, and loyalty initiatives. Collaborate with product, marketing, operations, and finance to identify and prioritize analytics opportunities. Ensure data quality, governance, and proper use of data across projects. Mentor and guide junior analysts and data scientists on best practices and frameworks. Evaluate and implement new analytical methods, tools, and data sources to enhance decision-making. Champion a culture of ethical, evidence-based decision-making aligned with Starbucks values. Required Skills Data science Statistical analysis A/B testing and experimentation Predictive modeling Machine learning SQLPython or RData visualization (Tableau/Power BI) Data storytelling and communication Business intelligence and reporting
11/01/2026
Full time
Starbucks is seeking a Senior/Lead Decision Scientist to drive data-informed strategy across our global coffeehouse business. In this role, you'll translate complex data into clear insights that improve customer experience, store operations, and digital engagement. You'll design experiments, develop predictive and prescriptive models, and build dashboards that guide leaders in merchandising, pricing, and loyalty initiatives. Partnering with cross-functional teams, you'll define metrics, validate hypotheses, and tell compelling data stories. You'll mentor junior analysts, champion data quality and governance, and help shape a culture of evidence-based, ethical decision-making at scale. Responsibilities Lead design and execution of experiments and A/B tests to optimize customer experience, pricing, and promotions. Develop predictive and prescriptive models to support demand forecasting, personalization, and store operations. Translate complex data into clear, actionable insights and narratives for business and executive stakeholders. Build and maintain dashboards, reports, and self-service analytics tools for cross-functional teams. Define and refine key performance metrics for digital, retail, and loyalty initiatives. Collaborate with product, marketing, operations, and finance to identify and prioritize analytics opportunities. Ensure data quality, governance, and proper use of data across projects. Mentor and guide junior analysts and data scientists on best practices and frameworks. Evaluate and implement new analytical methods, tools, and data sources to enhance decision-making. Champion a culture of ethical, evidence-based decision-making aligned with Starbucks values. Required Skills Data science Statistical analysis A/B testing and experimentation Predictive modeling Machine learning SQLPython or RData visualization (Tableau/Power BI) Data storytelling and communication Business intelligence and reporting
Starbucks is seeking a Senior/Lead Decision Scientist to drive data-informed strategy across our global coffee and food operations. In this role, you will use advanced analytics, experimentation, and predictive modeling to optimize pricing, product mix, and store performance. You'll partner with operations, marketing, and digital teams to translate complex data into clear insights, dashboards, and recommendations that enhance guest experience and profitability. You will mentor junior analysts, promote data literacy, and help shape a culture of ethical, responsible data use aligned with Starbucks' community and sustainability values. Responsibilities Develop and deploy advanced analytics, forecasting, and predictive models to inform pricing, product, and operational decisions. Design and analyze A/B tests and experiments to evaluate initiatives across stores and digital channels. Build clear dashboards and self-service BI tools for stakeholders in operations, marketing, and digital. Translate complex data into actionable insights and strategic recommendations for senior leaders. Partner with cross-functional teams to define metrics, KPIs, and measurement frameworks. Mentor and guide junior analysts and data scientists, promoting best practices in analytics and coding. Ensure data quality, governance, and ethical use of customer and operational data. Communicate findings through compelling presentations, visualizations, and written summaries. Collaborate with technology teams to improve data pipelines, tooling, and analytics platforms. Support long-term analytics roadmap aligned with Starbucks' sustainability, community, and growth goals. Required Skills SQLPython or RStatistical modeling Predictive analytics A/B testing and experimentation Machine learning fundamentals Data visualization (e.g., Tableau, Power BI) Big data tools (e.g., Spark, Hive) Dashboarding and reporting Business intelligence in retail/hospitality Data storytelling Forecasting and time-series analysis
11/01/2026
Full time
Starbucks is seeking a Senior/Lead Decision Scientist to drive data-informed strategy across our global coffee and food operations. In this role, you will use advanced analytics, experimentation, and predictive modeling to optimize pricing, product mix, and store performance. You'll partner with operations, marketing, and digital teams to translate complex data into clear insights, dashboards, and recommendations that enhance guest experience and profitability. You will mentor junior analysts, promote data literacy, and help shape a culture of ethical, responsible data use aligned with Starbucks' community and sustainability values. Responsibilities Develop and deploy advanced analytics, forecasting, and predictive models to inform pricing, product, and operational decisions. Design and analyze A/B tests and experiments to evaluate initiatives across stores and digital channels. Build clear dashboards and self-service BI tools for stakeholders in operations, marketing, and digital. Translate complex data into actionable insights and strategic recommendations for senior leaders. Partner with cross-functional teams to define metrics, KPIs, and measurement frameworks. Mentor and guide junior analysts and data scientists, promoting best practices in analytics and coding. Ensure data quality, governance, and ethical use of customer and operational data. Communicate findings through compelling presentations, visualizations, and written summaries. Collaborate with technology teams to improve data pipelines, tooling, and analytics platforms. Support long-term analytics roadmap aligned with Starbucks' sustainability, community, and growth goals. Required Skills SQLPython or RStatistical modeling Predictive analytics A/B testing and experimentation Machine learning fundamentals Data visualization (e.g., Tableau, Power BI) Big data tools (e.g., Spark, Hive) Dashboarding and reporting Business intelligence in retail/hospitality Data storytelling Forecasting and time-series analysis
Avanos Medical seeks an Associate Director IT Global Data Analytics, Innovation & Digital Experimentation to lead data strategy across our global medtech business. In this role, you will define and deliver advanced analytics, AI/ML, and data engineering solutions that optimize clinical, commercial, and operational decisions. You will own the data experimentation roadmap, partner with business and clinical leaders to identify high value use cases, and oversee end to end project delivery. Responsibilities include managing a technical team, governing data platforms, ensuring data quality, and championing innovation while maintaining security, privacy, and regulatory compliance in a patient-focused environment. Responsibilities Lead global data analytics and digital experimentation strategy for Avanos Medical Identify, prioritize, and deliver high-value analytics and AI/ML use cases across business functions Oversee design and implementation of data platforms, pipelines, and governance frameworks Manage and mentor a technical team of data engineers, scientists, and analysts Ensure data quality, integrity, security, and regulatory compliance in a healthcare context Partner with clinical, commercial, and operations leaders to translate needs into technical solutions Drive experimentation frameworks (A/B testing, pilots, POCs) to validate data-driven initiatives Report performance, insights, and ROI of analytics programs to senior leadership Collaborate with IT and cybersecurity to maintain resilient, scalable data infrastructure Champion a culture of innovation, continuous improvement, and patient-focused decision-making Required Skills Data analytics strategy Data engineering (ETL/ELT, data pipelines) Cloud data platforms (AWS, Azure, or GCP) SQL and relational databases Python or R for analytics and data science Machine learning and AI concepts BI and data visualization tools (Tableau, Power BI, etc.) A/B testing and experimentation frameworks Data governance, security, and privacy (including healthcare context) Project and stakeholder management Statistical analysis and modeling Data architecture and warehousing
11/01/2026
Full time
Avanos Medical seeks an Associate Director IT Global Data Analytics, Innovation & Digital Experimentation to lead data strategy across our global medtech business. In this role, you will define and deliver advanced analytics, AI/ML, and data engineering solutions that optimize clinical, commercial, and operational decisions. You will own the data experimentation roadmap, partner with business and clinical leaders to identify high value use cases, and oversee end to end project delivery. Responsibilities include managing a technical team, governing data platforms, ensuring data quality, and championing innovation while maintaining security, privacy, and regulatory compliance in a patient-focused environment. Responsibilities Lead global data analytics and digital experimentation strategy for Avanos Medical Identify, prioritize, and deliver high-value analytics and AI/ML use cases across business functions Oversee design and implementation of data platforms, pipelines, and governance frameworks Manage and mentor a technical team of data engineers, scientists, and analysts Ensure data quality, integrity, security, and regulatory compliance in a healthcare context Partner with clinical, commercial, and operations leaders to translate needs into technical solutions Drive experimentation frameworks (A/B testing, pilots, POCs) to validate data-driven initiatives Report performance, insights, and ROI of analytics programs to senior leadership Collaborate with IT and cybersecurity to maintain resilient, scalable data infrastructure Champion a culture of innovation, continuous improvement, and patient-focused decision-making Required Skills Data analytics strategy Data engineering (ETL/ELT, data pipelines) Cloud data platforms (AWS, Azure, or GCP) SQL and relational databases Python or R for analytics and data science Machine learning and AI concepts BI and data visualization tools (Tableau, Power BI, etc.) A/B testing and experimentation frameworks Data governance, security, and privacy (including healthcare context) Project and stakeholder management Statistical analysis and modeling Data architecture and warehousing
Lead the Pricing Analytics team in optimizing vehicle pricing and purchasing decisions through data science and analysis. Partner with business leaders to set goals, monitor model performance, respond to market changes, resolve system issues, and present recommendations that drive business results. Responsibilities Develop associates and promote engagement across the Pricing team. Partner with business leaders to define KPIs, measurable goals, and management systems. Monitor pricing models and identify when strategy changes are needed to reflect market conditions. Create systems to assess model performance, including alerts and regular review meetings. Respond to and resolve pricing system issues. Lead data scientists in using models to create business value and present recommendations to stakeholders. Required Skills Large-scale data analysis SQLPython Data science modeling Pricing analytics Model performance monitoring KPI development Strategic analysis Market analysis Analytical evaluation
10/31/2026
Full time
Lead the Pricing Analytics team in optimizing vehicle pricing and purchasing decisions through data science and analysis. Partner with business leaders to set goals, monitor model performance, respond to market changes, resolve system issues, and present recommendations that drive business results. Responsibilities Develop associates and promote engagement across the Pricing team. Partner with business leaders to define KPIs, measurable goals, and management systems. Monitor pricing models and identify when strategy changes are needed to reflect market conditions. Create systems to assess model performance, including alerts and regular review meetings. Respond to and resolve pricing system issues. Lead data scientists in using models to create business value and present recommendations to stakeholders. Required Skills Large-scale data analysis SQLPython Data science modeling Pricing analytics Model performance monitoring KPI development Strategic analysis Market analysis Analytical evaluation
HCA Healthcare is seeking a Senior Data Usability Analyst to join our Data Science & AI team in Healthcare & Medical Services. In this role, you will translate complex clinical and operational data into intuitive, trustworthy, and actionable information products that empower caregivers and leaders. You will partner with data engineers, data scientists, and clinicians to profile data, define business rules, improve data quality, and optimize data models and dashboards for usability and adoption. You will champion data standards, documentation, and governance while mentoring analysts and supporting strategic, patient-centered analytics initiatives. Responsibilities Collaborate with clinicians, operations, and data teams to translate business needs into usable data products Profile and validate clinical and operational data to ensure accuracy, completeness, and consistency Define and maintain business rules, data definitions, and documentation to improve data understanding Design and optimize dashboards, reports, and semantic layers for usability and adoption Identify and drive data quality improvements and standardization across sources and domains Partner with data engineers and data scientists to shape data models and pipelines for analytics use Champion data governance, metadata, and best practices in data usability across the organization Mentor junior analysts and contribute to analytics standards and reusable assets Support strategic analytics and AI initiatives focused on patient-centered care and operational excellence Communicate insights, limitations, and data usability considerations to technical and non-technical stakeholders Required Skills SQLPython or RData visualization (Tableau, Power BI, or similar) Data modeling Data quality and profiling Healthcare data standards (e.g., HL7, FHIR, ICD, CPT) ETL/ELT concepts Statistics and exploratory data analysis Requirements gathering and stakeholder management Data documentation and governance
10/02/2026
Full time
HCA Healthcare is seeking a Senior Data Usability Analyst to join our Data Science & AI team in Healthcare & Medical Services. In this role, you will translate complex clinical and operational data into intuitive, trustworthy, and actionable information products that empower caregivers and leaders. You will partner with data engineers, data scientists, and clinicians to profile data, define business rules, improve data quality, and optimize data models and dashboards for usability and adoption. You will champion data standards, documentation, and governance while mentoring analysts and supporting strategic, patient-centered analytics initiatives. Responsibilities Collaborate with clinicians, operations, and data teams to translate business needs into usable data products Profile and validate clinical and operational data to ensure accuracy, completeness, and consistency Define and maintain business rules, data definitions, and documentation to improve data understanding Design and optimize dashboards, reports, and semantic layers for usability and adoption Identify and drive data quality improvements and standardization across sources and domains Partner with data engineers and data scientists to shape data models and pipelines for analytics use Champion data governance, metadata, and best practices in data usability across the organization Mentor junior analysts and contribute to analytics standards and reusable assets Support strategic analytics and AI initiatives focused on patient-centered care and operational excellence Communicate insights, limitations, and data usability considerations to technical and non-technical stakeholders Required Skills SQLPython or RData visualization (Tableau, Power BI, or similar) Data modeling Data quality and profiling Healthcare data standards (e.g., HL7, FHIR, ICD, CPT) ETL/ELT concepts Statistics and exploratory data analysis Requirements gathering and stakeholder management Data documentation and governance
HCA Healthcare seeks a Senior Data Usability Analyst to join our Data Science & AI team transforming healthcare data into actionable, clinician-friendly insights. You will partner with product, clinical, and engineering teams to assess data quality, standardize definitions, and design intuitive data models, semantic layers, and metrics that improve trust and adoption. Responsibilities include profiling complex healthcare datasets, optimizing data workflows, documenting data standards, and translating user requirements into scalable solutions that support patient-centered care and operational excellence across our hospitals and services. Responsibilities Profile, validate, and monitor data quality across clinical and operational datasets Design and maintain intuitive data models, semantic layers, and standardized metrics Translate clinician and business requirements into scalable data solutions Collaborate with data engineers, data scientists, and product teams to improve data usability Document data definitions, lineage, and usage guidelines for analytics consumers Lead data governance and standardization efforts to ensure consistency and trust Develop dashboards and tools that promote self-service analytics adoption Conduct user research and feedback sessions to refine data products Support AI and advanced analytics initiatives with well-structured, reliable data Mentor junior analysts on data usability best practices and healthcare data standards Required Skills SQLData modeling and dimensional modeling Data quality assessment and profiling Healthcare data standards (ICD, CPT, HL7, FHIR, etc.) Data visualization (Tableau, Power BI, or similar) Metadata management and data cataloging Requirements gathering and stakeholder management ETL/ELT and data pipeline concepts Cloud data platforms (AWS, Azure, or GCP) Statistical analysis and exploratory data analysis
10/02/2026
Full time
HCA Healthcare seeks a Senior Data Usability Analyst to join our Data Science & AI team transforming healthcare data into actionable, clinician-friendly insights. You will partner with product, clinical, and engineering teams to assess data quality, standardize definitions, and design intuitive data models, semantic layers, and metrics that improve trust and adoption. Responsibilities include profiling complex healthcare datasets, optimizing data workflows, documenting data standards, and translating user requirements into scalable solutions that support patient-centered care and operational excellence across our hospitals and services. Responsibilities Profile, validate, and monitor data quality across clinical and operational datasets Design and maintain intuitive data models, semantic layers, and standardized metrics Translate clinician and business requirements into scalable data solutions Collaborate with data engineers, data scientists, and product teams to improve data usability Document data definitions, lineage, and usage guidelines for analytics consumers Lead data governance and standardization efforts to ensure consistency and trust Develop dashboards and tools that promote self-service analytics adoption Conduct user research and feedback sessions to refine data products Support AI and advanced analytics initiatives with well-structured, reliable data Mentor junior analysts on data usability best practices and healthcare data standards Required Skills SQLData modeling and dimensional modeling Data quality assessment and profiling Healthcare data standards (ICD, CPT, HL7, FHIR, etc.) Data visualization (Tableau, Power BI, or similar) Metadata management and data cataloging Requirements gathering and stakeholder management ETL/ELT and data pipeline concepts Cloud data platforms (AWS, Azure, or GCP) Statistical analysis and exploratory data analysis
Job Description Job Description About Medical Guardian: Medical Guardian is a fast-growing digital health and safety company on a mission to help people live a life without limits. With 13 consecutive years on the Inc. 5000 list of Fastest Growing Companies, we are redefining what it means to age confidently and independently. We support over 625,000 members nationwide with life-saving emergency response systems and remote patient monitoring solutions. Trusted by families, healthcare providers, and care managers, our work is powered by a culture of innovation, compassion, and purpose. Mission: This role is focused on building and leading the data engineering foundation that powers real-time decisioning, operational applications, analytics, ML/AI model development, and data services across Medical Guardian. The Principal Data Engineer will own the design, delivery, and maturity of production-grade data pipelines and data platforms, with a primary emphasis on real-time streaming, IoT telemetry, Databricks, Azure, data services for APIs and microservices, and reliable data products for downstream consumption. Role Summary: We are looking for a Principal Data Engineer to serve as a hands-on technical and people leader for data engineering, data platform architecture, real-time streaming, and production data services. This role will focus on designing, building, operating, and improving data pipelines and data products while also bringing principal-level judgment to architecture, stakeholder shaping, delivery priorities, team management, and production readiness. This is a hands-on engineering leadership role first. The ideal candidate should be comfortable spending significant time working directly with Databricks, Spark, SQL, Python/PySpark, Azure services, streaming architectures, data quality frameworks, pipeline automation, CI/CD, and production troubleshooting. They should also be able to operate with the maturity of a principal-level leader: shaping unclear requirements, making pragmatic technical decisions, managing and mentoring engineers, and driving work forward without waiting for perfect specifications. This is a fast-moving, startup-like environment. Requirements may be incomplete, priorities may evolve, and the right candidate will help create clarity while building quickly. We need someone who can move from ambiguous business need to reliable data capability with urgency, discipline, and ownership. Stakeholder shaping is a critical part of this role. The Principal Data Engineer should be able to work directly with business, product, software engineering, analytics, ML/AI, operations, and leadership stakeholders to define what data needs to exist, how it should be consumed, what production guarantees are required, and how success should be measured. A background in commercial software, SaaS, digital products, healthtech, fintech, IoT, data platforms, or other product-driven environments is strongly preferred. We want someone who understands that data pipelines and data services are not just technical artifacts. They are product capabilities that support real users, real workflows, operational decisions, ML/AI systems, APIs, analytics, and measurable business outcomes. Key Responsibilities: Hands-On Data Engineering and Platform Development Design, build, optimize, and operate production-grade batch and streaming data pipelines on Azure and Databricks, with a primary focus on real-time IoT and telemetry use cases within a Medallion architecture. Develop ETL/ELT workflows to ingest, transform, validate, and serve large volumes of structured, semi-structured, unstructured, and streaming data. Build and maintain reliable data products, data services, APIs, and microservices that support operational applications, analytics, software engineering, and ML/AI teams. Use Python, PySpark, Spark SQL, SQL, Delta Lake, Databricks Workflows, CI/CD, and related tools to build maintainable, testable, and observable data systems. Troubleshoot complex production pipeline issues across Databricks, Azure, streaming systems, APIs, and source systems, including root cause analysis, corrective action, and prevention planning. Move quickly from rough business need to prototype, pilot, and production-ready data capability while maintaining appropriate engineering discipline. Real-Time Streaming, IoT Telemetry, and Operational Data Services Lead the design and delivery of real-time streaming ingestion and processing patterns for connected medical device telemetry, event data, and operational data feeds. Implement streaming solutions using Azure Event Hubs, Azure Stream Analytics, Databricks, Delta Lake, and related Azure integration patterns. Design cost-effective throughput, partitioning, delivery, retention, and replay strategies for high-volume event and telemetry workloads. Create consumption patterns that support APIs, microservices, operational applications, near-real-time decisioning, analytics, and ML/AI use cases. Define reliability, latency, quality, observability, and supportability expectations for production streaming systems. Databricks, Lakehouse, and Data Platform Architecture Set direction for Databricks-based data engineering patterns, including Medallion architecture, Delta Lake, Spark optimization, data modeling, data quality, and reusable pipeline design. Optimize production Databricks pipelines using PySpark, Spark SQL, Delta Lake, partitioning strategies, caching, shuffle optimization, cluster/job configuration, and cost-aware design. Establish practical standards for pipeline structure, code organization, testing, deployment, monitoring, documentation, and ownership. Partner with data platform, security, infrastructure, and engineering teams to ensure the data platform is scalable, secure, reliable, and aligned with enterprise architecture. Make pragmatic architecture tradeoffs between speed, durability, cost, governance, performance, and downstream business impact. Stakeholder Shaping and Cross-Functional Partnership Work directly with business, product, analytics, ML/AI, operations, software engineering, and leadership stakeholders to clarify what data is needed, why it matters, how it will be used, and what success looks like. Translate ambiguous business needs into concrete data requirements, data product definitions, architecture options, delivery priorities, and implementation plans. Ask practical questions early: who will use the data, what decision or workflow does it support, what latency and quality are required, what happens if the data is wrong or late, and how will we know the capability is creating value? Help the organization avoid becoming a data ticket factory by shaping solutions, not just executing requests. Communicate architecture decisions, tradeoffs, risks, dependencies, and delivery options clearly to technical and non-technical stakeholders. Team Management and Principal-Level Technical Leadership Manage, mentor, and develop data engineers, providing clear expectations, technical guidance, prioritization support, feedback, and accountability. Provide technical leadership through hands-on example, strong engineering judgment, clear recommendations, and pragmatic decision-making. Lead design reviews, code reviews, production readiness reviews, incident reviews, and architecture discussions across data engineering initiatives. Establish and improve engineering standards for data quality, testing, CI/CD, observability, documentation, runbooks, cost management, privacy, and security. Proactively identify platform risks, data gaps, unclear ownership, operational weaknesses, and opportunities to improve reliability, scalability, and delivery speed. Influence without relying only on formal authority by building trust, framing tradeoffs, and helping cross-functional teams get to decisions. ML/AI, Analytics, and GenAI Enablement Partner with ML engineers, data scientists, analytics engineers, and analysts to deliver reliable data pipelines, feature pipelines, training datasets, scoring inputs, and feedback loops. Support the data foundation for predictive models, risk scores, operational decisioning, GenAI workflows, RAG, document intelligence, summarization, and AI-enabled automation. Help define data contracts, model-ready datasets, feature definitions, lineage, and monitoring expectations for ML/AI and analytics use cases. Ensure downstream consumers understand the meaning, freshness, quality, limitations, and appropriate use of the data products they depend on. Governance, Data Quality, Security, and Production Operations Apply privacy-first, security-aware, and governance-aligned practices for regulated, sensitive, and operationally critical data. Design and implement data quality checks, validation rules, anomaly detection, schema expectations, alerting, and operational monitoring. Ensure production pipelines and services are supportable, observable, documented, recoverable, and aligned with business continuity needs. Drive incident response and continuous improvement for data platform and pipeline issues, including root cause analysis and preventative remediation. Balance innovation with reliability, compliance, privacy, cost discipline, and operational usefulness. Required Qualifications: 10+ years of professional experience in data engineering . click apply for full job details
09/29/2026
Full time
Job Description Job Description About Medical Guardian: Medical Guardian is a fast-growing digital health and safety company on a mission to help people live a life without limits. With 13 consecutive years on the Inc. 5000 list of Fastest Growing Companies, we are redefining what it means to age confidently and independently. We support over 625,000 members nationwide with life-saving emergency response systems and remote patient monitoring solutions. Trusted by families, healthcare providers, and care managers, our work is powered by a culture of innovation, compassion, and purpose. Mission: This role is focused on building and leading the data engineering foundation that powers real-time decisioning, operational applications, analytics, ML/AI model development, and data services across Medical Guardian. The Principal Data Engineer will own the design, delivery, and maturity of production-grade data pipelines and data platforms, with a primary emphasis on real-time streaming, IoT telemetry, Databricks, Azure, data services for APIs and microservices, and reliable data products for downstream consumption. Role Summary: We are looking for a Principal Data Engineer to serve as a hands-on technical and people leader for data engineering, data platform architecture, real-time streaming, and production data services. This role will focus on designing, building, operating, and improving data pipelines and data products while also bringing principal-level judgment to architecture, stakeholder shaping, delivery priorities, team management, and production readiness. This is a hands-on engineering leadership role first. The ideal candidate should be comfortable spending significant time working directly with Databricks, Spark, SQL, Python/PySpark, Azure services, streaming architectures, data quality frameworks, pipeline automation, CI/CD, and production troubleshooting. They should also be able to operate with the maturity of a principal-level leader: shaping unclear requirements, making pragmatic technical decisions, managing and mentoring engineers, and driving work forward without waiting for perfect specifications. This is a fast-moving, startup-like environment. Requirements may be incomplete, priorities may evolve, and the right candidate will help create clarity while building quickly. We need someone who can move from ambiguous business need to reliable data capability with urgency, discipline, and ownership. Stakeholder shaping is a critical part of this role. The Principal Data Engineer should be able to work directly with business, product, software engineering, analytics, ML/AI, operations, and leadership stakeholders to define what data needs to exist, how it should be consumed, what production guarantees are required, and how success should be measured. A background in commercial software, SaaS, digital products, healthtech, fintech, IoT, data platforms, or other product-driven environments is strongly preferred. We want someone who understands that data pipelines and data services are not just technical artifacts. They are product capabilities that support real users, real workflows, operational decisions, ML/AI systems, APIs, analytics, and measurable business outcomes. Key Responsibilities: Hands-On Data Engineering and Platform Development Design, build, optimize, and operate production-grade batch and streaming data pipelines on Azure and Databricks, with a primary focus on real-time IoT and telemetry use cases within a Medallion architecture. Develop ETL/ELT workflows to ingest, transform, validate, and serve large volumes of structured, semi-structured, unstructured, and streaming data. Build and maintain reliable data products, data services, APIs, and microservices that support operational applications, analytics, software engineering, and ML/AI teams. Use Python, PySpark, Spark SQL, SQL, Delta Lake, Databricks Workflows, CI/CD, and related tools to build maintainable, testable, and observable data systems. Troubleshoot complex production pipeline issues across Databricks, Azure, streaming systems, APIs, and source systems, including root cause analysis, corrective action, and prevention planning. Move quickly from rough business need to prototype, pilot, and production-ready data capability while maintaining appropriate engineering discipline. Real-Time Streaming, IoT Telemetry, and Operational Data Services Lead the design and delivery of real-time streaming ingestion and processing patterns for connected medical device telemetry, event data, and operational data feeds. Implement streaming solutions using Azure Event Hubs, Azure Stream Analytics, Databricks, Delta Lake, and related Azure integration patterns. Design cost-effective throughput, partitioning, delivery, retention, and replay strategies for high-volume event and telemetry workloads. Create consumption patterns that support APIs, microservices, operational applications, near-real-time decisioning, analytics, and ML/AI use cases. Define reliability, latency, quality, observability, and supportability expectations for production streaming systems. Databricks, Lakehouse, and Data Platform Architecture Set direction for Databricks-based data engineering patterns, including Medallion architecture, Delta Lake, Spark optimization, data modeling, data quality, and reusable pipeline design. Optimize production Databricks pipelines using PySpark, Spark SQL, Delta Lake, partitioning strategies, caching, shuffle optimization, cluster/job configuration, and cost-aware design. Establish practical standards for pipeline structure, code organization, testing, deployment, monitoring, documentation, and ownership. Partner with data platform, security, infrastructure, and engineering teams to ensure the data platform is scalable, secure, reliable, and aligned with enterprise architecture. Make pragmatic architecture tradeoffs between speed, durability, cost, governance, performance, and downstream business impact. Stakeholder Shaping and Cross-Functional Partnership Work directly with business, product, analytics, ML/AI, operations, software engineering, and leadership stakeholders to clarify what data is needed, why it matters, how it will be used, and what success looks like. Translate ambiguous business needs into concrete data requirements, data product definitions, architecture options, delivery priorities, and implementation plans. Ask practical questions early: who will use the data, what decision or workflow does it support, what latency and quality are required, what happens if the data is wrong or late, and how will we know the capability is creating value? Help the organization avoid becoming a data ticket factory by shaping solutions, not just executing requests. Communicate architecture decisions, tradeoffs, risks, dependencies, and delivery options clearly to technical and non-technical stakeholders. Team Management and Principal-Level Technical Leadership Manage, mentor, and develop data engineers, providing clear expectations, technical guidance, prioritization support, feedback, and accountability. Provide technical leadership through hands-on example, strong engineering judgment, clear recommendations, and pragmatic decision-making. Lead design reviews, code reviews, production readiness reviews, incident reviews, and architecture discussions across data engineering initiatives. Establish and improve engineering standards for data quality, testing, CI/CD, observability, documentation, runbooks, cost management, privacy, and security. Proactively identify platform risks, data gaps, unclear ownership, operational weaknesses, and opportunities to improve reliability, scalability, and delivery speed. Influence without relying only on formal authority by building trust, framing tradeoffs, and helping cross-functional teams get to decisions. ML/AI, Analytics, and GenAI Enablement Partner with ML engineers, data scientists, analytics engineers, and analysts to deliver reliable data pipelines, feature pipelines, training datasets, scoring inputs, and feedback loops. Support the data foundation for predictive models, risk scores, operational decisioning, GenAI workflows, RAG, document intelligence, summarization, and AI-enabled automation. Help define data contracts, model-ready datasets, feature definitions, lineage, and monitoring expectations for ML/AI and analytics use cases. Ensure downstream consumers understand the meaning, freshness, quality, limitations, and appropriate use of the data products they depend on. Governance, Data Quality, Security, and Production Operations Apply privacy-first, security-aware, and governance-aligned practices for regulated, sensitive, and operationally critical data. Design and implement data quality checks, validation rules, anomaly detection, schema expectations, alerting, and operational monitoring. Ensure production pipelines and services are supportable, observable, documented, recoverable, and aligned with business continuity needs. Drive incident response and continuous improvement for data platform and pipeline issues, including root cause analysis and preventative remediation. Balance innovation with reliability, compliance, privacy, cost discipline, and operational usefulness. Required Qualifications: 10+ years of professional experience in data engineering . click apply for full job details
Job Description Job Description Our mission is to create the Experience of a Lifetime for our employees, so they can, in turn, create the Experience of a Lifetime for our guests. We own and operate the most renowned destination resorts in the world as well as regional and local ski areas outside major cities, and connect them all through one unrivaled network. We are looking for ambitious leaders, innovators and creators to join our talented team. If you're ready to pursue your fullest potential, we want to get to know you! Candidates for year-round positions are reviewed on a rolling basis. Applications will be accepted up to 90 days after the posting date, or until the position is filled (whichever is first). Job Summary: We are looking for a curious, driven, innovative machine learning engineer who takes initiative to solve problems and create environments that accelerate the development, deployment, and usage of data science models and AI to drive greater organizational impact. The Data Science & Data Engineering team within the Enterprise Analytics organization builds data assets, predictive models, analytical applications, and platforms across the organization. Our team collaborates with business stakeholders, analysts, and technology teams to tackle high-impact use cases with state-of-the-art models and tools to grow the business, streamline costs, and improve guest experiences. Job Specifications: Starting Wage: $140,000 - $185,000 + Annual Bonus Employment Type: Year Round Shift Type: Full Time hours Minimum Age: At least 18 years of age Housing Availability: No Job Responsibilities: Productionize ML models developed by data science into reliable, monitored, maintainable systems. Build model data foundations that ensure training, inference, monitoring, and analytics data are trustworthy and scalable. Architect ML platform patterns in Databricks that bring reliability, consistency, governance, performance, and cost discipline to ML and data workflows. Identify and scope opportunities for ML engineering across the business for high-impact. Develop reusable tools , libraries, standards, documentation, and production-readiness practices to enable data science and data engineering teams. Develop analytical and model-powered applications that turn data and ML outputs into usable business workflows for end users. Prepare the platform for future AI engineering , including LLM and agent-based systems, as the organization matures. Provide technical leadership and mentoring across engineering, architecture, and development including design and code reviews. Job Requirements: Technical Skills: Quantitative Foundation : B.S. degree in a quantitative field (e.g., Computer Science, Mathematics, Statistics, Economics, Operations Research, Engineering). Software Engineering Fundamentals: write clean, modular, testable, maintainable code and understand how to structure production-grade systems rather than one-off notebooks or scripts. Python and SQL Proficiency: strong in Python and SQL for building data pipelines, automation, model integrations, analytical workflows, and production services. Data Modeling and Pipeline Design: understand how to design reliable, well-structured data assets, including curated tables, feature datasets, batch pipelines, orchestration, data quality checks, and lineage. ML Lifecycle Fluency: understand the full model lifecycle: data collection, exploration, model development, validation, deployment, monitoring, retraining, and retirement. Production ML Patterns: understand core MLOps patterns such as model registries, feature/data versioning, reproducible environments, testing/validation, monitoring, and rollback. Cloud and Platform Engineering: You are comfortable working in cloud-based data and ML environments and understand the foundations of permissions, environments, jobs, services, storage, networking, and cost-aware architecture. Databricks Expertise : You're familiar and experienced with the core parts of Spark, Unity Catalog, Delta Lake, Databricks Workflows, MLflow, model registry patterns, job/cluster optimization, and governance. DevOps Practices: You use modern engineering practices such as Git, CI/CD, automated testing, code review, dependency management, environment management, and observability. Application Development : You can build applications, APIs, dashboards, or workflow tools that sit on top of data and model outputs. System Design: You can reason through tradeoffs across reliability, latency, scale, cost, governance, maintainability, and ease of use. Soft Skills: Curious : bring intellectual curiosity, an inquisitive nature, and a desire to deepen your knowledge and continue learning. Ownership : take responsibility to proactively advance projects, contribute to the organization, and develop the best solutions. Communication: explain technical concepts, risks, tradeoffs, and recommendations clearly to technical and non-technical audiences. Collaboration: work effectively cross-functionally with data scientists, data engineers, analysts, application engineers, product partners, and business stakeholders. Pragmatism : You know how to balance ideal architecture with business urgency, team maturity, operational constraints, and the need to ship. Preferred qualifications: A graduate degree (Masters or PhD) in a quantitative field Experience with dbt (Core) for modular data modeling, including testing, documentation, and dependency management Experience with AI engineer to use, build, and monitor agentic solutions The expected Total Compensation for this role is $140,000 - $185,000 + Annual Bonus. Individual compensation decisions are based on a variety of factors. Job Benefits Ski/Mountain Perks! Free passes for employees, employee discounted lift tickets for friends and family AND free ski lessons MORE employee discounts on lodging, food, gear, and mountain shuttles 401(k) Retirement Plan Employee Assistance Program Excellent training and professional development Full Time roles are eligible for the above, plus: Health Insurance; Medical Insurance, Dental Insurance, and Vision Insurance plans (for eligible seasonal employees after working 500 hours) Free ski passes for dependents Critical Illness and Accident plans Employees can work remotely from British Columbia, Washington D.C., and the 16 U.S. states in which we currently operate. This includes: California, Colorado, Indiana, Michigan, Minnesota, Missouri, New Hampshire, New York, Nevada, Ohio, Pennsylvania, Utah, Vermont, Washington State, Wisconsin, and Wyoming. Please note that the ability to work in person or off-site, and the particulars related to such work, are subject to change at any time; and, accordingly, the Company reserves the right to change its policies and/or require in-person/in-office work or off-site work at any time in its sole discretion. In completing this application, and when submitting related documentation, applicants may redact information that identifies their age, date of birth, and/or dates of attendance at or graduation from an educational institution. We follow all federal, state, and local laws including restrictions on child/minor labor. Minors hired into this position will not be asked or permitted to engage in any activities restricted to adult workers. Vail Resorts is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, protected veteran status or any other status protected by applicable law. Requisition ID 517322 Reference Date: 09/05/2026 Job Code Function: Data Science
09/28/2026
Full time
Job Description Job Description Our mission is to create the Experience of a Lifetime for our employees, so they can, in turn, create the Experience of a Lifetime for our guests. We own and operate the most renowned destination resorts in the world as well as regional and local ski areas outside major cities, and connect them all through one unrivaled network. We are looking for ambitious leaders, innovators and creators to join our talented team. If you're ready to pursue your fullest potential, we want to get to know you! Candidates for year-round positions are reviewed on a rolling basis. Applications will be accepted up to 90 days after the posting date, or until the position is filled (whichever is first). Job Summary: We are looking for a curious, driven, innovative machine learning engineer who takes initiative to solve problems and create environments that accelerate the development, deployment, and usage of data science models and AI to drive greater organizational impact. The Data Science & Data Engineering team within the Enterprise Analytics organization builds data assets, predictive models, analytical applications, and platforms across the organization. Our team collaborates with business stakeholders, analysts, and technology teams to tackle high-impact use cases with state-of-the-art models and tools to grow the business, streamline costs, and improve guest experiences. Job Specifications: Starting Wage: $140,000 - $185,000 + Annual Bonus Employment Type: Year Round Shift Type: Full Time hours Minimum Age: At least 18 years of age Housing Availability: No Job Responsibilities: Productionize ML models developed by data science into reliable, monitored, maintainable systems. Build model data foundations that ensure training, inference, monitoring, and analytics data are trustworthy and scalable. Architect ML platform patterns in Databricks that bring reliability, consistency, governance, performance, and cost discipline to ML and data workflows. Identify and scope opportunities for ML engineering across the business for high-impact. Develop reusable tools , libraries, standards, documentation, and production-readiness practices to enable data science and data engineering teams. Develop analytical and model-powered applications that turn data and ML outputs into usable business workflows for end users. Prepare the platform for future AI engineering , including LLM and agent-based systems, as the organization matures. Provide technical leadership and mentoring across engineering, architecture, and development including design and code reviews. Job Requirements: Technical Skills: Quantitative Foundation : B.S. degree in a quantitative field (e.g., Computer Science, Mathematics, Statistics, Economics, Operations Research, Engineering). Software Engineering Fundamentals: write clean, modular, testable, maintainable code and understand how to structure production-grade systems rather than one-off notebooks or scripts. Python and SQL Proficiency: strong in Python and SQL for building data pipelines, automation, model integrations, analytical workflows, and production services. Data Modeling and Pipeline Design: understand how to design reliable, well-structured data assets, including curated tables, feature datasets, batch pipelines, orchestration, data quality checks, and lineage. ML Lifecycle Fluency: understand the full model lifecycle: data collection, exploration, model development, validation, deployment, monitoring, retraining, and retirement. Production ML Patterns: understand core MLOps patterns such as model registries, feature/data versioning, reproducible environments, testing/validation, monitoring, and rollback. Cloud and Platform Engineering: You are comfortable working in cloud-based data and ML environments and understand the foundations of permissions, environments, jobs, services, storage, networking, and cost-aware architecture. Databricks Expertise : You're familiar and experienced with the core parts of Spark, Unity Catalog, Delta Lake, Databricks Workflows, MLflow, model registry patterns, job/cluster optimization, and governance. DevOps Practices: You use modern engineering practices such as Git, CI/CD, automated testing, code review, dependency management, environment management, and observability. Application Development : You can build applications, APIs, dashboards, or workflow tools that sit on top of data and model outputs. System Design: You can reason through tradeoffs across reliability, latency, scale, cost, governance, maintainability, and ease of use. Soft Skills: Curious : bring intellectual curiosity, an inquisitive nature, and a desire to deepen your knowledge and continue learning. Ownership : take responsibility to proactively advance projects, contribute to the organization, and develop the best solutions. Communication: explain technical concepts, risks, tradeoffs, and recommendations clearly to technical and non-technical audiences. Collaboration: work effectively cross-functionally with data scientists, data engineers, analysts, application engineers, product partners, and business stakeholders. Pragmatism : You know how to balance ideal architecture with business urgency, team maturity, operational constraints, and the need to ship. Preferred qualifications: A graduate degree (Masters or PhD) in a quantitative field Experience with dbt (Core) for modular data modeling, including testing, documentation, and dependency management Experience with AI engineer to use, build, and monitor agentic solutions The expected Total Compensation for this role is $140,000 - $185,000 + Annual Bonus. Individual compensation decisions are based on a variety of factors. Job Benefits Ski/Mountain Perks! Free passes for employees, employee discounted lift tickets for friends and family AND free ski lessons MORE employee discounts on lodging, food, gear, and mountain shuttles 401(k) Retirement Plan Employee Assistance Program Excellent training and professional development Full Time roles are eligible for the above, plus: Health Insurance; Medical Insurance, Dental Insurance, and Vision Insurance plans (for eligible seasonal employees after working 500 hours) Free ski passes for dependents Critical Illness and Accident plans Employees can work remotely from British Columbia, Washington D.C., and the 16 U.S. states in which we currently operate. This includes: California, Colorado, Indiana, Michigan, Minnesota, Missouri, New Hampshire, New York, Nevada, Ohio, Pennsylvania, Utah, Vermont, Washington State, Wisconsin, and Wyoming. Please note that the ability to work in person or off-site, and the particulars related to such work, are subject to change at any time; and, accordingly, the Company reserves the right to change its policies and/or require in-person/in-office work or off-site work at any time in its sole discretion. In completing this application, and when submitting related documentation, applicants may redact information that identifies their age, date of birth, and/or dates of attendance at or graduation from an educational institution. We follow all federal, state, and local laws including restrictions on child/minor labor. Minors hired into this position will not be asked or permitted to engage in any activities restricted to adult workers. Vail Resorts is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, protected veteran status or any other status protected by applicable law. Requisition ID 517322 Reference Date: 09/05/2026 Job Code Function: Data Science
Job Description Job Description Company Description About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at . on LinkedIn, Facebook, Instagram, X and YouTube. Job Description While the AI innovation race in Biopharma is focused on Drug discovery, Product Development/ CMC represents the next barrier/ bottleneck. The complexity of biological systems, the rigor of regulatory expectations, the pace of pipeline growth, and the enormous value at stake make this one of the highest-leverage domains for applied data science and AI in the entire pharmaceutical value chain. We here at BTS - PDST, are building a dedicated, AI-native team that is driving cutting edge programs across early stage, late stage and commercial product development to accelerate E2E product development and launch, maximize yields of block buster products. Through our deep collaboration with PDST scientists we are boldly reimagining how Abbvie can bring our pipeline products and lifesaving drugs to patients faster, safer and in cost effective manner fueled by AI. Principal Data Engineer is a highly technical, AI-native role responsible for designing, building, and operating production-grade data pipelines and data products that power AI/ML, analytics, and automation across AbbVie's CMC and manufacturing ecosystem. This role is embedded inside PDST and works at the frontier of pharmaceutical data engineering. You will integrate and harmonize data from the full spectrum of manufacturing and development systems - including MES, historians, LIMS, QMS, ERP, and instrument platforms - and transform it into reliable, governed, semantically rich data assets that data scientists, process engineers, and AI systems can actually use. Enterprise-scale scope: Enterprise-scale biologics portfolio spanning clinical, commercial, and lifecycle stages Building AI playbook for the future: First-in-AbbVie and first-in-biologics analytical approaches; you build the AI playbook for the future Growth and Impact: Direct impact on regulatory submissions, commercial readiness, and manufacturing decisions through deep cross-functional exposure to manufacturing, quality, regulatory, and scientific leadership Mission: Every model you build helps ensure safe, reliable medicines reach patients at scale Responsibilities Data Ingestion & Integration Design and implement scalable, robust data ingestion pipelines that connect CMC and manufacturing source systems - including MES (Manufacturing Execution Systems), process historians, LIMS, QMS, ERP platforms, and instrument data sources - to centralized and federated data environments. Build connectors, adapters, and integration layers that handle the heterogeneous data formats, protocols, and latency profiles characteristic of pharmaceutical manufacturing environments. Support both batch and real-time/streaming data patterns, selecting appropriate architectures based on use case requirements. Data Harmonization & Semantic Modeling Develop and maintain harmonized data models and ontologies that bring consistency to CMC and manufacturing data across sites, systems, and modalities. Execute semantic mapping efforts that align source system fields, units, and identifiers to enterprise data standards and scientific meaning. Collaborate with process scientists, analytical chemists, and manufacturing engineers to ensure data models accurately reflect domain reality. Data Quality, Observability & Governance Implement automated data quality controls, validation frameworks, and anomaly detection mechanisms across pipeline layers. Build and maintain data lineage documentation and metadata infrastructure, enabling full traceability from source system to AI model input. Establish pipeline observability practices - monitoring, alerting, SLA tracking - to ensure data product reliability in production. Support data governance practices aligned with GxP requirements, 21 CFR Part 11, and AbbVie data standards. AI/ML Enablement & Data Product Development Architect and deliver governed, versioned, reusable data products purpose-built for AI/ML consumption, including feature stores, curated datasets, and vector-ready data layers for RAG and LLM applications. Partner closely with data scientists, ML engineers, and process modelers to understand model data requirements and translate them into reliable, scalable data infrastructure. Accelerate AI program delivery by eliminating data bottlenecks - not by workarounds, but by solving root causes structurally. Platform & Operational Enablement Contribute to the design and evolution of PDST's cloud-based data platform, including lakehouse architecture, data cataloging, access control, and compute infrastructure. Write and maintain infrastructure-as-code, CI/CD pipelines, and automated testing frameworks for data systems. Support platform onboarding of new CMC data domains and manufacturing sites, ensuring consistent application of standards and patterns. Provide operational support for production data pipelines, maintaining uptime and data freshness commitments. Stakeholder Engagement & Scientific Leadership Influence technical decision-making without formal authority - earning trust through scientific rigor, transparent methodology, and demonstrated business impact. Qualifications Required: Bachelor's Degree Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a closely related technical field plus 6 years' experience; Master's Degree plus 5 years' experience; PhD plus 0 years' experience. Respective years of hands-on experience designing and building enterprise-grade data pipelines, integration workflows, and data products in complex, multi-source environments. Expert-level proficiency in Python for data engineering tasks - pipeline development, transformation logic, data validation, and automation. Strong SQL skills across modern analytical and transactional databases; comfort with both ANSI SQL and platform-specific dialects. Demonstrated experience with cloud data platforms (AWS, Azure, or GCP) and modern data stack components - including tools such as dbt, Spark, Airflow, Databricks, Snowflake, or equivalents. Develop ETL/ELT pipelines using tools such as Informatica, Talend, Apache NiFi, and cloud-native services (e.g., AWS Glue, Azure Data Factory). Implement master data management (MDM), metadata management, and data cataloging solutions to ensure proper data lineage, accessibility, and compliance. Set and enforce standards for API development and data integration (REST, GraphQL, OData), enabling seamless integration using microservices architectures. Ownership orientation: you define your own problem space, drive solutions to completion, and hold yourself accountable to outcomes - not just outputs. Solution-architect instinct: you think before you build, consider the full landscape of available approaches, and choose tools based on fit-for-purpose reasoning rather than familiarity or trend. Scientific integrity: you build models you can explain, defend, and improve - and you apply the same standard to the work of others. Influence through credibility: you earn the confidence of scientists, engineers, and quality professionals by being right, being clear, and being useful - not by title or volume. Bias for impact: you are drawn to problems where the stakes are high and the analytical opportunity is real, and you are energized rather than intimidated by ambiguity. Preferred: 3+ years of hands-on experience designing and building enterprise-grade data pipelines, integration workflows, and data products in complex, multi-source environments. Familiarity with technology transfer workflows, process characterization study design, or commercial process validation (PPQ/PV) in a biologics or pharmaceutical context. Experience in pharmaceutical, biotech, or other regulated life sciences manufacturing environments. Familiarity with GxP data principles, 21 CFR Part 11 compliance, or data integrity requirements in regulated industries. Prior exposure to manufacturing source systems such as MES, process historians (e.g., OSIsoft PI/AVEVA), LIMS, QMS, or ERP platforms Experience building data infrastructure for AI/ML programs - including feature engineering pipelines, model training datasets, or vector/embedding data layers for RAG architectures. Knowledge of biologics manufacturing processes (e.g., upstream cell culture, downstream purification, fill-finish) or CMC development workflows. Familiarity with data mesh, data fabric, or federated data architecture patterns. Experience with graph databases, knowledge graphs, or ontology frameworks applied to scientific or manufacturing data. Contributions to open-source data tooling or demonstrated engagement with the modern data engineering community. Design logical, physical, and conceptual data models using modeling tools (e.g., Erwin, PowerDesigner, dbt). . click apply for full job details
09/26/2026
Full time
Job Description Job Description Company Description About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at . on LinkedIn, Facebook, Instagram, X and YouTube. Job Description While the AI innovation race in Biopharma is focused on Drug discovery, Product Development/ CMC represents the next barrier/ bottleneck. The complexity of biological systems, the rigor of regulatory expectations, the pace of pipeline growth, and the enormous value at stake make this one of the highest-leverage domains for applied data science and AI in the entire pharmaceutical value chain. We here at BTS - PDST, are building a dedicated, AI-native team that is driving cutting edge programs across early stage, late stage and commercial product development to accelerate E2E product development and launch, maximize yields of block buster products. Through our deep collaboration with PDST scientists we are boldly reimagining how Abbvie can bring our pipeline products and lifesaving drugs to patients faster, safer and in cost effective manner fueled by AI. Principal Data Engineer is a highly technical, AI-native role responsible for designing, building, and operating production-grade data pipelines and data products that power AI/ML, analytics, and automation across AbbVie's CMC and manufacturing ecosystem. This role is embedded inside PDST and works at the frontier of pharmaceutical data engineering. You will integrate and harmonize data from the full spectrum of manufacturing and development systems - including MES, historians, LIMS, QMS, ERP, and instrument platforms - and transform it into reliable, governed, semantically rich data assets that data scientists, process engineers, and AI systems can actually use. Enterprise-scale scope: Enterprise-scale biologics portfolio spanning clinical, commercial, and lifecycle stages Building AI playbook for the future: First-in-AbbVie and first-in-biologics analytical approaches; you build the AI playbook for the future Growth and Impact: Direct impact on regulatory submissions, commercial readiness, and manufacturing decisions through deep cross-functional exposure to manufacturing, quality, regulatory, and scientific leadership Mission: Every model you build helps ensure safe, reliable medicines reach patients at scale Responsibilities Data Ingestion & Integration Design and implement scalable, robust data ingestion pipelines that connect CMC and manufacturing source systems - including MES (Manufacturing Execution Systems), process historians, LIMS, QMS, ERP platforms, and instrument data sources - to centralized and federated data environments. Build connectors, adapters, and integration layers that handle the heterogeneous data formats, protocols, and latency profiles characteristic of pharmaceutical manufacturing environments. Support both batch and real-time/streaming data patterns, selecting appropriate architectures based on use case requirements. Data Harmonization & Semantic Modeling Develop and maintain harmonized data models and ontologies that bring consistency to CMC and manufacturing data across sites, systems, and modalities. Execute semantic mapping efforts that align source system fields, units, and identifiers to enterprise data standards and scientific meaning. Collaborate with process scientists, analytical chemists, and manufacturing engineers to ensure data models accurately reflect domain reality. Data Quality, Observability & Governance Implement automated data quality controls, validation frameworks, and anomaly detection mechanisms across pipeline layers. Build and maintain data lineage documentation and metadata infrastructure, enabling full traceability from source system to AI model input. Establish pipeline observability practices - monitoring, alerting, SLA tracking - to ensure data product reliability in production. Support data governance practices aligned with GxP requirements, 21 CFR Part 11, and AbbVie data standards. AI/ML Enablement & Data Product Development Architect and deliver governed, versioned, reusable data products purpose-built for AI/ML consumption, including feature stores, curated datasets, and vector-ready data layers for RAG and LLM applications. Partner closely with data scientists, ML engineers, and process modelers to understand model data requirements and translate them into reliable, scalable data infrastructure. Accelerate AI program delivery by eliminating data bottlenecks - not by workarounds, but by solving root causes structurally. Platform & Operational Enablement Contribute to the design and evolution of PDST's cloud-based data platform, including lakehouse architecture, data cataloging, access control, and compute infrastructure. Write and maintain infrastructure-as-code, CI/CD pipelines, and automated testing frameworks for data systems. Support platform onboarding of new CMC data domains and manufacturing sites, ensuring consistent application of standards and patterns. Provide operational support for production data pipelines, maintaining uptime and data freshness commitments. Stakeholder Engagement & Scientific Leadership Influence technical decision-making without formal authority - earning trust through scientific rigor, transparent methodology, and demonstrated business impact. Qualifications Required: Bachelor's Degree Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a closely related technical field plus 6 years' experience; Master's Degree plus 5 years' experience; PhD plus 0 years' experience. Respective years of hands-on experience designing and building enterprise-grade data pipelines, integration workflows, and data products in complex, multi-source environments. Expert-level proficiency in Python for data engineering tasks - pipeline development, transformation logic, data validation, and automation. Strong SQL skills across modern analytical and transactional databases; comfort with both ANSI SQL and platform-specific dialects. Demonstrated experience with cloud data platforms (AWS, Azure, or GCP) and modern data stack components - including tools such as dbt, Spark, Airflow, Databricks, Snowflake, or equivalents. Develop ETL/ELT pipelines using tools such as Informatica, Talend, Apache NiFi, and cloud-native services (e.g., AWS Glue, Azure Data Factory). Implement master data management (MDM), metadata management, and data cataloging solutions to ensure proper data lineage, accessibility, and compliance. Set and enforce standards for API development and data integration (REST, GraphQL, OData), enabling seamless integration using microservices architectures. Ownership orientation: you define your own problem space, drive solutions to completion, and hold yourself accountable to outcomes - not just outputs. Solution-architect instinct: you think before you build, consider the full landscape of available approaches, and choose tools based on fit-for-purpose reasoning rather than familiarity or trend. Scientific integrity: you build models you can explain, defend, and improve - and you apply the same standard to the work of others. Influence through credibility: you earn the confidence of scientists, engineers, and quality professionals by being right, being clear, and being useful - not by title or volume. Bias for impact: you are drawn to problems where the stakes are high and the analytical opportunity is real, and you are energized rather than intimidated by ambiguity. Preferred: 3+ years of hands-on experience designing and building enterprise-grade data pipelines, integration workflows, and data products in complex, multi-source environments. Familiarity with technology transfer workflows, process characterization study design, or commercial process validation (PPQ/PV) in a biologics or pharmaceutical context. Experience in pharmaceutical, biotech, or other regulated life sciences manufacturing environments. Familiarity with GxP data principles, 21 CFR Part 11 compliance, or data integrity requirements in regulated industries. Prior exposure to manufacturing source systems such as MES, process historians (e.g., OSIsoft PI/AVEVA), LIMS, QMS, or ERP platforms Experience building data infrastructure for AI/ML programs - including feature engineering pipelines, model training datasets, or vector/embedding data layers for RAG architectures. Knowledge of biologics manufacturing processes (e.g., upstream cell culture, downstream purification, fill-finish) or CMC development workflows. Familiarity with data mesh, data fabric, or federated data architecture patterns. Experience with graph databases, knowledge graphs, or ontology frameworks applied to scientific or manufacturing data. Contributions to open-source data tooling or demonstrated engagement with the modern data engineering community. Design logical, physical, and conceptual data models using modeling tools (e.g., Erwin, PowerDesigner, dbt). . click apply for full job details
