Data Scientist - Clinical AI

Position Summary CVS Health's Analytics & Behavior Change (A&BC) team is working to solve 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. The A&BC organization is expanding its Clinical Data Science & AI team. As a Data Scientist - Clinical AI, you will activate CVS Health's clinical data repository to improve outcomes across multiple lines of business and use cases, acting as the bridge between clinical data assets and analysts, data scientists, and business partners to ensure data is accessible, well-documented, fit for purpose, and aligned with clinical and regulatory standards. Responsibilities 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. Develop predictive analytics solutions. Build and validate predictive models using 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. Communicate findings clearly. Present methodology, results, and recommendations to technical and non‑technical stakeholders through well‑crafted visualizations, notebooks, and presentations. 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 integrate into products and decision‑making processes. Stay current and stay curious. Continuously evaluate emerging techniques in NLP, foundation models, and clinical AI, bringing new ideas to the team and advocating approaches grounded in evidence. Uphold data governance standards. Ensure all work complies with HIPAA, data privacy regulations, and internal data stewardship policies, especially when handling PHI and unstructured clinical text. Required Qualifications 2+ years of experience in data science, machine learning, or applied NLP—preferably in healthcare or a similarly regulated domain. Hands‑on experience with NLP—including text preprocessing, tokenization, named entity recognition, text classification, topic modeling, or similar techniques applied to real‑world unstructured data. Practical experience with LLMs and/or SLMs—prompt engineering, fine‑tuning, RAG architectures, evaluation frameworks, or deploying language models in production or research settings. Strong foundation in traditional machine learning—supervised and unsupervised methods, feature engineering, model selection, cross‑validation, and performance evaluation. Best coding practices—version control (Git/Github), clean reproducible code, and well‑organized repositories. Deep EDA skills—systematically exploring datasets, identifying data quality issues, surfacing insights, and making informed decisions about modeling. Proficiency in Python (pandas, scikit‑learn, PyTorch or TensorFlow, Hugging Face Transformers) and SQL for working with large‑scale healthcare datasets. Experience with cloud‑based data and ML platforms—preferably Google Cloud Platform (GCP) with BigQuery, Vertex AI, or equivalent. Excellent presentation and communication skills—clearly explaining your work, why it was built, and its business impact. Judgment and common sense—meeting deadlines, asking for help when stuck, and avoiding over‑engineering solutions. A genuine curiosity and desire to learn—reading papers, trying new tools, asking “why,” and staying focused on delivering solutions. Preferred Qualifications Experience working with clinical text data—clinical notes, discharge summaries, pathology reports, or similar unstructured healthcare documents. Knowledge of clinical coding systems and terminologies (ICD‑10, SNOMED‑CT, LOINC, RxNorm, CPT, NDC, UMLS) and their relevance to NLP pipelines. Familiarity 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. Experience with model evaluation in clinical contexts—including sensitivity/specificity tradeoffs, clinical validation, and responsible AI practices in healthcare. Familiarity with MLOps practices—model versioning, experiment tracking, CI/CD for ML, model monitoring. Experience working directly with clinical stakeholders (physicians, nurses, clinical operation teams) and tailoring presentations to different audiences. Privacy, security, and compliance experience—HIPAA/HITRUST, de‑identification/tokenization, PHI handling. Education Bachelor’s degree in health informatics, biostatistics, computer science, data science, mathematics, biomedical informatics, or a related field—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 Science or a related field is a plus—but not a substitute for demonstrated ability to ship real‑world solutions. Clinical background (RN, PharmD, MD, or similar) with transition into data science or AI is a genuine differentiator for this role. Benefits We take pride in offering a comprehensive and competitive mix of pay and benefits that supports members’ physical, emotional, and financial well‑being. This full‑time position is eligible for medical, dental, vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility. Pay Range $79,310.00 – $158,620.00 Positions are eligible for a CVS Health bonus, commission or short‑term incentive program in addition to the base salary range listed above. Anticipated Weekly Hours: 40 Time Type: Full time We anticipate the application window for this opening will close on: 07/31/2026 Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws. #J-18808-Ljbffr