Senior Data Scientist - Clinical AI

Position Summary 

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 AI, 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: 

• Extract signal from unstructured clinical text. Apply NLP and language model techniques to clinical notes, CCD documents, and other free-text clinical data to generate structured, actionable features for downstream analytics and predictive models. 

• Build and fine-tune Small Language Models (SLMs). Design, train, and evaluate domain-specific SLMs tailored to clinical use cases — balancing performance, cost, latency, and compliance requirements. 

• Utilize LLMs where applicable. Leverage large language models where they add clear value (e.g., training data creation, entity extraction, zero-shot classification) while knowing when traditional ML, rules-based approaches, or simpler statistical methods are the right tool for the job. 

• Develop predictive analytics solutions. Build and validate predictive models using both classical ML (gradient boosting, logistic regression, survival analysis) and modern deep learning approaches to support clinical decision-making and population health initiatives. 

• Conduct rigorous Exploratory Data Analysis (EDA). Deeply explore clinical datasets — structured and unstructured — to uncover patterns, assess data quality, identify feature candidates, and inform modeling strategy before jumping to solutions. 

• Communicate findings clearly. Present methodology, results, and recommendations to technical and non-technical stakeholders through well-crafted visualizations, notebooks, and presentations. Translate complex AI/ML concepts into language that clinical and business partners can act on. 

• Collaborate across teams. Work with machine learning engineers, data en