Senior Data Scientist - Clinical Informatics (Claims Specialization)

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.

Position Summary

CVS Health's Analytics & Behavior Change (A&BC) team is an organization working to solve some of the most challenging problems at the intersection of technology and healthcare. A&BC 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&BC 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. 

As a Senior 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. 

You will: 

• Serve as a subject matter expert in clinical data, including claims data, 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. 

• Build 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. 

• Develop 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. 

• Build queries, dashboards, and data visualizations to effectively communicate data quality metrics, data availability, and clinical insights to technical and non-technical stakeholders. 

• 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. 

• Understand data governance initiatives, including compliance with HIPAA, data privacy regulations, and internal data stewardship policies. 

• Develop and deliver presentations and consultations to existing and prospective