Lead Data Scientist - Clinical Informatics (Claims Specialization)

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 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. 

You will: 

• 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 informatics. 

• 7+ years of relevant experience in clinical informatics, healthcare analytics, or clinical data management. 

• Deep expertise in clinical data types and structures, including medical claims, pharmacy claims, lab results, clinical notes, and administrative healthcare data. 

• Knowledge of clinical coding systems and terminologies, such as ICD-10, CPT, HCPCS, SNOMED-CT, LOINC, NDC, and RxNorm. 

• Experience designing and documenting data models, taxonomies, or classification frameworks for clinical or healthcare data. 

• Proven ability to enable and support downstream data consumers (analysts, data scientists, business users) through documentation, training, and consultative support. 

• Experience leading cross-functional projects from concept to delivery by coordinating across clinical, technical, and business stakeholders. 

• Proficiency with SQL and experience working with large-scale healthcare datasets. 

• Experience using cloud-based data platforms, preferably Google Cloud Platform (GCP) tools including BigQuery, for querying, transforming, and managing data. 

• Strong understanding of data quality principles, including validation, profiling, and monitoring of healthcare data. 

• Excellent written and verbal communication skills, including the ability to explain complex clinical data concepts to both technical and non-technical audiences. 

• Ability to anticipate and resolve roadblocks throughout a project lifecycle, balancing competing priorities across multiple stakeholders. 

• Strong experience with medical claims (professional and institutional), pharmacy claims, and eligibility/enrollment data, including understanding of adjudication, adjustments, and claims completeness considerations.  

• Familiarity with claims-based analytics, including total cost of care, utilization metrics, risk adjustment (HCC), and episode groupers. 

• Strong understanding of interoperability and large‑scale data harmonization across administrative sources (e.g., medical & pharmacy claims, enrollment/eligibility files, provider files) and across common standards such as X12, NCPDP, FHIR, and OMOP. 

• Expertise in claims lifecycle and payer workflows, including claim submission, adjudication, pricing, remittance, utilization management, and benefits configuration. 

• Experience working with standardized administrative code systems (e.g., ICD‑10‑CM, CPT/HCPCS, DRG, NDC). 

• Hands-on experience with ETL pipelines from payer sources into normalized data standards, preferably OMOP CDM with cost and payer domains. 

• Bachelor’s degree in health informatics, Public Health, Nursing, Health Information Management, Computer Science, Statistics, or a related quantitative or clinical field—or an equivalent combination of formal education and experience. 

• Master's degree or higher in Health Informatics, Biomedical Informatics, Clinical Informatics, Public Health, Epidemiology, or a related field is strongly preferred. 

• Clinical background (RN, PharmD, MD, or similar) with transition into informatics/analytics is highly valued.