Position Summary

CVSHealth's Analytics & Behavior Change (A&BC) team is an organization working to solve some of the most challenging problems at the intersection of technologyand healthcare. A&BCleveragesadvanced 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 aSeniorData Scientist - ClinicalAI, 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.

• UtilizeLLMs 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.Buildand 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 andunstructured — to uncover patterns, assess data quality,identifyfeature candidates, and inform modeling strategybefore jumping to solutions.

• Communicate findings clearly.Presentmethodology, 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 withmachine learningengineers,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. Bringnew ideasto the team, prototype rapidly, and advocate for approaches grounded in evidence rather than hype.

• Uphold data governance standards.Ensure all workcomplies withHIPAA, 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 break down on messy real-world data, and can make principled architecture decisions across text preprocessing, NER, classification, topic modeling, and beyond. 

• Proven experience designing and deploying LLM/SLM-based systems – prompt engineering, fine-tuning, RAG architecture, evaluation frameworks, or deploying language models in production settings 

• Strong foundation in traditional machine learning — supervised and unsupervised methods, feature engineering, model selection, cross-validation, and performance evaluation. 

• Best coding practices– youcommitquality code. You use version control as a matter of instinct; write code others can buildonand youunderstand that a well-structured, reproducible codebaseis part of a production-grade deliverable.

• AdvancedEDA skills— ability to systematically explore datasets,identifydata quality issues, surface insights, and make informed decisions before jumping intomodeling approaches.

• Expert-levelPython (pandas, scikit-learn,PyTorchor TensorFlow, Hugging Face Transformers) and SQL for working with large-scale healthcare datasets.You write performant, maintainable code and know when to optimize and when not to.

• Experience with cloud-based data and ML platforms,preferably Google Cloud Platform (GCP) — BigQuery, Vertex AI, or equivalent.

• Excellent presentation and communication skills— you can stand in front of a room and clearly explain what you built, why you built it that way, and what it means for the business.

• Judgment and common sense— you know when a LLM is the righttool and when it is overkill. You hold yourself and others todeadlinesand you are able to direct yourjunior team memberswhen theyare stuck.

• A genuine curiosity and desire to learn — you read papers, you try new tools, you ask "why," andyou'reenergized by problems you haven't solved before.You know when a rabbit hole is worth diving into and when to pull back, stay focused, and deliver.

Preferred Qualifications 

• Significant experience working with clinical text data — clinical notes, discharge summaries, pathology reports, or similar unstructured healthcare documents. 

• Working knowledge of clinical coding systems and terminologies (ICD-10, SNOMED-CT, LOINC, RxNorm, CPT, NDC, UMLS) and their relevance to NLP pipelines. 

• Hands-on experience with clinical data standards (HL7, FHIR, CCD/C-CDA) and common data models (e.g., OMOP). 

• Experience building or contributing to clinical NLP pipelines — entity extraction, relation extraction, negation detection, or section segmentation from clinical narratives. 

• Deep understanding of model evaluation in clinical contexts — understanding of sensitivity/specificity tradeoffs, clinical validation, and responsible AI practices in healthcare. 

• Understand and help guide MLOps — model versioning, experiment tracking, CI/CD for ML, model monitoring. 

• Experience working directly with clinical stakeholders (physicians, nurses, clinical operation teams, etc.) and tailoring presentations, findings, and recommendations to the appropriate audience level – from executive summaries for leadership to detailed methodology reviews for technical notes. 

• Privacy, security, and compliance experience: HIPAA/HITRUST, de-identification/tokenization, PHI/PII handling. 

Education

• Bachelor’s degree in health informatics,biostatistics, computer science, data science mathematics, biomedical informatics, or related—or an equivalent combination of formal education and experience.

• Master's degree or higher in Health Informatics, Biomedical Informatics, Clinical Informatics, Public Health, Epidemiology,Data Scienceor a related field is a plus– but not a substitute for demonstratedability to ship real-world solutions

• Clinical background (RN, PharmD, MD, or similar) with transition intodata science