Interested in this Data Scientist role at CVS Health?
Apply Now →Skills & Technologies
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.
Role Overview
Aetna, a CVS Health company, is a leading health innovation organization focused on transforming healthcare delivery and improving outcomes. As part of the growing Medicare Stars organization, we are seeking a Principal Data Scientist to lead advanced analytics, forecasting, and scientific modeling efforts that drive enterprise\-level Star Ratings performance.
This role is a senior individual contributor and thought leader responsible for defining and scaling the next generation of Stars forecasting, cutpoint analytics, and performance modeling. The individual will lead development of predictive models, statistical frameworks, and AI\-enabled analytics solutions to inform strategic decision\-making across Medicare Stars domains.
This position will own end\-to\-end analytic strategy—from data engineering and model design to executive insights and business impact—and will serve as a key partner to leadership across Product, Clinical, Quality, and Data organizations.
Position Summary
Strategic Leadership \& Analytics
- Lead the design and execution of enterprise\-level Medicare Stars forecasting models, including multi\-year projections (e.g., 2027–2029 stars scenarios).
- Define scientific frameworks for Stars cutpoint derivation, incorporating CMS methodology, guardrails, and national distribution analysis.
- Partner with senior leadership to translate forecast insights into actionable business strategies and risk mitigation plans.
- Provide thought leadership in Stars performance analytics, influencing roadmap, prioritization, and investments.
- Advanced Modeling \& Forecasting: Develop and maintain end\-to\-end forecasting models across domains: CAHPS, HEDIS, HOS, Patient Safety / Pharmacy measures
- Build year\-end projections based on historical trends, seasonality, and industry benchmarks.
- Perform sensitivity analysis and scenario modeling (Low/Mid/High models) to quantify risk and opportunity.
- Design predictive models leveraging statistical methods, machine learning, and time\-series forecasting.
- Translate regulatory changes into model enhancements and impact assessments.
- Lead efforts to interpret and operationalize CMS technical guidance (e.g., disaster adjustments, guardrails, cutpoint methodology).
- Experience in building year\-end forecasts using historical trends and industry benchmarks
Data, Technology Leadership \& Technical Skills
- Architect and implement scalable analytics solutions using:
+ Snowflake (Snowpark, data pipelines, governance)
+ Python / SQL\-based modeling frameworks
+ ML/AI and LLM\-enabled analytics (including RAG architectures)
- Build integrated data ecosystems combining:
+ Structured clinical and operational data
+ Survey and experience data (CAHPS/HOS)
+ External industry benchmarks
- Data Visualization: Ability to create compelling visualizations using tools like Tableau, Power BI, and Snowflake and any other BI tools
- Author Snowpark/Python/SQL transformations, Streams \& Tasks, and job orchestration to enable near\-real\-time and batch analytical workloads.
- Strong expertise in: Time\-series forecasting, Machine learning \& predictive modeling and Statistical inference and simulation modeling
- Database Management: Knowledge of SQL and NoSQL databases for efficient data storage and retrieval.
- Enable self\-service analytics and executive dashboards for real\-time decision support.
- Provide technical leadership and mentorship to data scientists and analysts.
- Integrate Snowflake with LLMs (OpenAI or equivalent) for tasks such as summarization, question\-answering, classification, and code\-generation—using External Functions, Snowpark, and/or secure API patterns.
- Establish best practices for modeling, validation, documentation, and version control.
- Ensure compliance with data governance, privacy, and regulatory standards.
- Advanced proficiency in Snowflake, Python, SQL, and statistical modeling frameworks
- Experience with Snowflake, cloud data platforms, and large\-scale analytics pipelines
- Familiarity with LLM\-enabled analytics and retrieval\-augmented generation (RAG)
- Strong quantitative and problem\-solving abilities
- Ability to translate complex data into clear business insights
- Expertise in building and validating predictive models
Leadership \& Communication
- Executive\-level communication and storytelling skills
- Proven ability to influence senior stakeholders
- Strong cross\-functional collaboration and leadership experience
Required Qualifications
- 10\+ years in data science, analytics, or forecasting roles
- Significant experience in Medicare Stars or healthcare quality analytics preferred
- Experience leading large\-scale forecasting or Stars performance programs
- Proven track record of delivering enterprise\-scale analytics solutions and business impact
Preferred Qualifications
- Certifications: Professional certifications in data science, machine learning, or business analytics can be beneficial.
- Experience with CMS Stars regulatory interpretation and analytics
- Prior work in Medicare Advantage or healthcare quality organizations
Education
- Education: A bachelor's degree in a related field such as Statistics, Mathematics, Data Science, Economics, Computer Science, or related field. Advanced degrees (Master's or Ph.D.) in these fields are highly desirable.
Why This Role
This is a highly visible leadership role where you will directly shape the organization’s Stars performance strategy, influence executive decision\-making, and drive measurable outcomes in quality and member experience.
Pay Range
The typical pay range for this role is:
$144,200\.00 \- $288,400\.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. This position also includes an award target in the company’s equity award program.
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: 07/20/2026
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
Salary Context
This $144K-$288K range is above the 75th percentile 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 ($216K) sits 12% above the category median. Disclosed range: $144K to $288K.
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
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.