Decision Scientist - Clinical Informatics; Clinical Data Standards

Decision Scientist
- Clinical Informatics (Clinical Data Standards)

We're building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you'll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize 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 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.

You will:

• 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.

Required Qualifications:

• 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 Big Query, for querying, transforming, and managing data.
• Understanding of data quality principles,…