Lead Data Scientist - Clinical Informatics; Clinical Data Standards

Overview

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. CVS Health is committed to 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&BC) team solves 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 CVS Health businesses. Our teams build next-generation data and AI products to power CVS Health for healthier outcomes for 100+ million customers.

The A&BC organization is growing its Clinical Data Science & AI team. Join us 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.

• Serve as a subject matter expert in clinical data, including CCD 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.
• Develop and govern 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.
• 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.
• 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.
• 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 CCD data, lab results, clinical notes, 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.
• 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 Big Query, 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…