Data Scientist - Clinical AI

Role Description

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.

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 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.

• 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.

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 — text preprocessing, tokenization, named entity recognition (NER), 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 – you use version control (Git/Github), commit your work regularly, write clean and reproducible code, and understand that well-organized repositories are as important as well-built models.
• Deep EDA skills — ability to systematically explore datasets, identify data quality issues, surface insights, and make informed decisions about modeling approach before writing a single line of model code.
• 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) — 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 understand that not every problem needs an LLM, you meet your deadlines, you ask for help when you're stuck, and you don't over-engineer solutions.
• A genuine curiosity and desire to learn — you read papers, you try new tools, you ask "why," and you're energized 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.

Requirements

• 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 — understanding of 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, 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 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 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.

Anticipated Weekly Hours

40

Time Type

Full time

Pay Range

The typical pay range for this role is: $79,310.00 - $158,620.00. This pay range represents the base hourly rate or base annual full-time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above.

Benefits

• Comprehensive benefits package designed to support the physical, emotional, and financial well-being of colleagues and their families.
• Medical, dental, and vision coverage.
• Paid time off.
• Retirement savings options.
• Wellness programs and other resources, based on eligibility.