Note: The job is a remote job and is open to candidates in USA. CVS Health is a company dedicated to improving health care experiences. They are seeking a Senior Data Scientist - Clinical AI to activate CVS Health's clinical data repository and enhance outcomes across various business lines by utilizing advanced analytics and machine learning techniques.
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 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
Skills
- 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 – you commit quality code. You use version control as a matter of instinct; write code others can build on and you understand that a well-structured, reproducible code base is part of a production-grade deliverable
- Advanced EDA skills— ability to systematically explore datasets, identify data quality issues, surface insights, and make informed decisions before jumping into modeling approaches
- Expert-level Python (pandas, scikit-learn, PyTorch or 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 right tool and when it is overkill. You hold yourself and others to deadlines and you are able to direct your junior team members when they are stuck
- 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
- 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
Benefits
- This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above.
- This full‑time position is eligible for a comprehensive benefits package designed to support the physical, emotional, and financial well‑being of colleagues and their families.
- The benefits for this position include medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility.
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