Decision Scientist - Clinical Informatics (Clinical Data Standards)

CVS Health•Carolina, NY

8d•Remote

About The Position

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 Decision Scientist - Clinical Informatics (Clinical Data Standards), 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.

Requirements

2+ years of relevant experience in clinical informatics, healthcare analytics, or clinical data management.
Familiar with clinical data types and structures, including CCD data, 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.
Ability to support downstream data consumers (analysts, data scientists, business users) through documentation, training, and consultative support.
Proficiency with SQL and experience working with large-scale healthcare datasets.
Familiar using cloud-based data platforms, preferably Google Cloud Platform (GCP) tools including BigQuery, for querying, transforming, and managing data.
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.

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).

Responsibilities

Become 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.
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.
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.
Learn data governance practices, including compliance with HIPAA, data privacy regulations, and internal data stewardship policies.
Stay current with clinical data standards (HL7, FHIR, ICD-10, SNOMED-CT, LOINC, CPT, NDC, RxNorm) and industry best practices in clinical informatics.