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About This Role
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 \- 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.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 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 foundationin 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.
- AdvancedEDA skills— ability to systematically explore datasets, identify data quality issues, surface insights, and make informed decisions before jumping into modeling approaches.
- Expert\-levelPython (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.
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 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 differentiate for this role.
Anticipated Weekly Hours
40Time Type
Full timePay Range
The typical pay range for this role is:
$101,970\.00 \- $203,940\.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.
Our people fuel our future. Our teams reflect the customers, patients, members and communities we serve and we are committed to fostering a workplace where every colleague feels valued and that they belong.
Great benefits for great people
We take pride in offering a comprehensive and competitive mix of pay and benefits that reflects our commitment to our colleagues and their families.
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.
Additional details about available benefits are provided during the application process and on Benefits Moments.
We anticipate the application window for this opening will close on: 10/02/2026
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
Salary Context
This $101K-$203K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At CVS Health, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($152K) sits 21% below the category median. Disclosed range: $101K to $203K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
CVS Health AI Hiring
CVS Health has 10 open AI roles right now. They're hiring across LLM Engineer, AI/ML Engineer, Data Scientist, AI Software Engineer. Positions span Hartford, CT, US, Richardson, TX, US, Woonsocket, RI, US. Compensation range: $144K - $288K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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