Senior Data Scientist - Clinical AI

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.

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 engineers, clinical informaticists, and business partners to ensure clinical data pipelines support AI/ML workflows and that model outputs are integrated into products and decision-making processes.
• Stay current and stay curious. Continuously evaluate emerging techniques in NLP, foundation models, and clinical AI. Bring new ideas to the team, prototype rapidly, and advocate for approaches grounded in evidence rather than hype.
• Uphold data governance standards. Ensure all work complies with HIPAA, data privacy regulations, and internal data stewardship policies, particularly when handling PHI and unstructured clinical text.

Required Qualifications

• 4+ years of experience in data science, machine learning, or applied NLP with meaningful depth in healthcare or a similarly regulated domain, and a track record of delivering production-grade work, not just research of prototypes.
• Deep, hands-on experience in NLP – you have built and shipped NLP systems end-to-end, not just experimented with them. You understand the tradeoffs between real approaches, know where standard techniques bre…