Lead Data Scientist Clinical Informatics

CVSHealth'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&BCleveragesadvanced 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 aLead 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 expertin clinical data, including claims, pharmacy, lab results, and clinical documentation, withdeepunderstanding of how to structure and apply this data to solve healthcare problems.

• Design andmaintainclinical data models, taxonomies, and classification frameworksthat enable consistent interpretation and use of clinical data across the organization.

• Develop and govern theclaimsdata feature store,establishingstandards, documentation, and best practices that accelerate adoption of clinical data for downstream analytics, reporting, and AI/ML use cases.

• Enable self-service analyticsby building well-documented, validated, and reusable data assets (tables, views, features) that empower analysts and data scientists to work independently with clinical data.

• Create andmaintaincomprehensive data documentation, including data dictionaries, lineage, business logic, known limitations, andappropriate useguidelines for clinical datasets.

• Partner with clinical, operational, and business stakeholdersto understand their data needs, translate requirements into data solutions, and ensure clinical data assets meet their analyticalobjectives.

• 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 frameworksfor 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 teamsto 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 consultationsto 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 experiencein clinical informatics, healthcare analytics, or clinical data management.

• Deepexpertisein 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, andRxNorm.

• Experience designing and documenting data models, taxonomies, or classification frameworksfor 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 projectsfrom concept to delivery by coordinating across clinical, technical, and business stakeholders.

• Proficiencywith SQLand experience working with large-scale healthcare datasets.

• Experience using cloud-based data platforms, preferably Google Cloud Platform (GCP) tools includingBigQuery, 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 toanticipateand resolve roadblocksthroughout 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,utilizationmetrics, 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.

• Expertisein claims lifecycle and payer workflows, including claim submission, adjudication, pricing, remittance,utilizationmanagement, 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.