Lead Data Scientist - Clinical Informatics (Claims Specialization)

Position Summary

We're building a world of health around every individual - shaping a more connected, convenient and compassionate health experience. At CVS Health®, you'll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger - helping to simplify health care one person, one family and one community at a time.

CVS Health's Analytics & Behavior Change (A&B) team is an organization working to solve some of the most challenging problems at the intersection of technology and healthcare. A&B leverages advanced analytics, clinical informatics, and hypothesis‑driven approaches to transform data into actionable, customer‑centric insights that drive growth, improve health outcomes, and expand access to healthcare across all CVS Health businesses. Our teams build next‑generation data and AI products that help power CVS Health to make healthier happen for 100+ million customers.

The A&B organization is looking to grow its Clinical Data Science & AI team. Join us as we embark on an exciting journey to drive a transformational shift in how CVS Health leverages clinical data and analytics to become the leader in consumer healthcare in the U.S.

Lead Data Scientist – Clinical Informatics (Claims Specialization)

As a Lead Data Scientist – Clinical Informatics (Claims Specialization), you are tasked with activating CVS Health's clinical data repository to improve outcomes across multiple lines of business and use cases. You will serve as a bridge between clinical data assets and the analysts, data scientists, and business partners who consume them—ensuring data is accessible, well‑documented, fit for purpose, and aligned with clinical and regulatory standards.

Key Responsibilities

• Serve as a subject matter expert in clinical data, including claims, pharmacy, lab results, and clinical documentation, with deep understanding of how to structure and apply this data to solve healthcare problems.
• Design and maintain clinical data models, taxonomies, and classification frameworks that enable consistent interpretation and use of clinical data across the organization.
• Develop and govern the claims data feature store, establishing standards, documentation, and best practices that accelerate adoption of clinical data for downstream analytics, reporting, and AI/ML use cases.
• Enable self‑service analytics by building well‑documented, validated, and reusable data assets (tables, views, features) that empower analysts and data scientists to work independently with clinical data.
• Create and maintain comprehensive data documentation, including data dictionaries, lineage, business logic, known limitations, and appropriate use guidelines for clinical datasets.
• Partner with clinical, operational, and business stakeholders to understand their data needs, translate requirements into data solutions, and ensure clinical data assets meet their analytical objectives.
• Lead and mentor data scientists, data analysts, and data engineers, providing guidance on clinical data interpretation, appropriate use, and best practices for working with healthcare data.
• Establish data quality frameworks for clinical data, including validation rules, anomaly detection, and monitoring processes to ensure data integrity and reliability.
• Translate clinical concepts into analytical frameworks, ensuring that business partners understand the capabilities and limitations of available clinical data.
• Collaborate with data engineering teams to inform data pipeline development, ensuring clinical data is ingested, transformed, and stored in ways that support downstream analytics needs.
• Contribute to data governance initiatives, including compliance with HIPAA, data privacy regulations, and internal data stewardship policies.
• Develop and deliver training, presentations, and consultations to existing and prospective data consumers on clinical data assets, appropriate use, and analytics opportunities.
• Stay current with clinical data standards (HL7, FHIR, ICD‑10, SNOMED‑CT, LOINC, CPT, NDC, RxNorm) and industry best practices in clinical…