Lead Data Scientist - Clinical Informatics (Clinical Data Standards)

CVS Health•New York, NY

19h•$130,295 - $260,590

About The Position

CVS Health's Analytics & Behavior Change (A&BC) team is dedicated to solving complex challenges at the intersection of technology and healthcare. We utilize advanced analytics, clinical informatics, and hypothesis-driven methods to transform data into actionable insights that enhance customer experiences, improve health outcomes, and broaden healthcare access across all CVS Health businesses. Our teams develop cutting-edge data and AI products that empower CVS Health to positively impact over 100 million customers. The A&BC organization is expanding its Clinical Data Science & AI team. We are seeking individuals to drive a significant transformation in how CVS Health utilizes clinical data and analytics to become a leader in consumer healthcare in the U.S. As a Lead Data Scientist - Clinical Informatics (Clinical Data Standards), your primary role is to activate CVS Health's clinical data repository to enhance outcomes across various business lines and use cases. You will act as a crucial link between clinical data assets and the consumers of this data, including analysts, data scientists, and business partners, ensuring data accessibility, thorough documentation, fitness for purpose, and adherence to clinical and regulatory standards.

Requirements

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 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.

Nice To Haves

Healthcare data platform experience with strong understanding of interoperability standards and harmonization at scale (OMOP/CCDA/FHIR)
Familiarity with clinical workflows and HIEs.
Experience using standardized clinical code systems (e.g., ICD-10, SNOMED CT, LOINC, RxNorm, UMLS) and their application within common data models (e.g., OMOP).
Experience in ETL design & implementation from heterogeneous clinical sources into different data standards preferably into the OMOP CDM.
Experience designing and implementing data quality frameworks, preferred to have experience with tools like Achilles, Data Quality Dashboard (DQDB), or equivalent custom frameworks.
Privacy, security, and compliance: HIPAA/HITRUST experience, de-identification/tokenization, PHI handling, and data access controls (column-level, row-level security).
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.

Responsibilities

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.