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 (Analytics Enablement), 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 CCD data, 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. 

• Build the clinical 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. 

• 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. 

• Maintain 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. 

Required Qualifications 

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

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

• Strong 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. 

• Expert with SQL and proficient with Python, 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. 

• Proven experience integrating clinical (CCD/OMOP/FHIR) and administrative (claims) data into unified, patient-centric data models, with deep understanding of the strengths, limitations, and complementary nature of each data type. 

• Experience with patient data normalization & standardization for patient attributes and cross source harmonization. 

• Hands-on experience reconciling clinical and claims data, including diagnosis alignment, medication reconciliation (prescribed vs. dispensed), and encounter/visit matching. 

• Experience integrating third-party and enrichment data sources, including SDOH indices (ADI, SVI), consumer/demographic data, mortality data, and provider reference data into patient-level datasets. 

• Expert knowledge of clinical and administrative coding systems, including ICD-10-CM/PCS, CPT/HCPCS, SNOMED-CT, RxNorm, NDC, LOINC, and NPI. 

• Experience with classification and grouping systems such as HCC, CCS, DRG, and therapeutic class hierarchies. 

• Experience designing patient-centric data models, feature stores, and dashboards that aggregate longitudinal data across sources, including demographics, encounters, conditions, medications, labs, utilization, cost, and enrichment attributes 

• Proven ability to enable downstream data consumers through analytics and well-documented, validated, and reusable data assets, with experience creating data dictionaries, lineage documentation, and self-service analytics layers. 

• Understanding of healthcare business contexts such as care management, value-based care, quality measurement (HEDIS, Stars), and population health. 

• 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.