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Position Summary
Aetna, a CVS Health company, is the nation's premier health innovation company helping people on their path to better health. We are building a new health care model that is easier to use, less expensive, and puts the consumer at the center of their care.
Aetna's Medicare Stars Team is growing and expanding! This is exciting opportunity to join a high performing, collaborative team as a Lead Data Scientist. This individual will focus on leveraging the Stars Data Ecosystem for Business Analytics for improved understanding of business patterns, predict future trends, and implement effective solutions to various challenges. This position will manage and be responsible for the successful delivery of scalable analytics solutions on Snowflake that combine structured and unstructured data, enable agentic document analytics at scale, and expose insights via natural language queries powered by LLMs (including OpenAI). Own end-to-end implementation: ingestion, unified data modeling, vectorization/embeddings, retrieval pipelines, Snowflake compute (Snowpark), and integration with LLM-driven applications. This role will be utilizing their expertise in statistical analysis, machine learning, and data visualization to analyze complex data sets, then derive actionable insights that inform business strategies and decisions. By leveraging data, they help the Stars organization understand patterns, predict future trends, and implement effective solutions to various challenges.
Key Responsibilities
Design and implement unified data pipelines in Snowflake that combine structured tables, semi-structured data (JSON/Parquet), and large collections of documents (PDF, DOCX, text).
Build agentic document analytics workflows: large-scale document ingestion, text extraction, cleaning, chunking, embeddings, vector stores, and efficient retrieval for analytic queries.
Implement Natural Language Query (NLQ) interfaces that translate user text prompts into analytic queries or retrieval flows and return explainable results.
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.
Create performant retrieval-augmented generation (RAG) architectures that leverage Snowflake-stored embeddings and external or internal vector indexes as appropriate.
Author Snowpark/Python/SQL transformations, Streams & Tasks, and job orchestration to enable near-real-time and batch analytical workloads.
Implement data modeling and governance patterns within Snowflake: schemas, role-based access control, masking, lineage, and metadata to support analytics and compliance.
Partner with product, ML/AI, BI, and engineering teams to translate business requirements into robust production-ready solutions.
Build monitoring, observability, and cost controls for compute, storage, and API usage related to document analytics and LLM integration.
Produce technical documentation, runbooks, and clear explanations of model/LLM behavior and limitations to non-technical stakeholders.
This position is a telework role, and candidates must be eligible to work in the United States.
Required Qualifications
Data Scientists must possess a diverse set of skills to excel in their roles. These include:
Technical Skills
Statistical Analysis: Proficiency in statistical methods and software, such as R, SAS, and Python, to analyze data.
Data Visualization: Ability to create compelling visualizations using tools like Tableau, Power BI, and D3.js.
Database Management: Knowledge of SQL and NoSQL databases for efficient data storage and retrieval.
Programming: Strong programming skills, particularly in languages such as Python, Java, and C++.
Design and implement unified data pipelines in Snowflake that combine structured tables, semi-structured data (JSON/Parquet), and large collections of documents (PDF, DOCX, text).
Build agentic document analytics workflows: large-scale document ingestion, text extraction, cleaning, chunking, embeddings, vector stores, and efficient retrieval for analytic queries.
Implement Natural Language Query (NLQ) interfaces that translate user text prompts into analytic queries or retrieval flows and return explainable results.
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.
Create performant retrieval-augmented generation (RAG) architectures that leverage Snowflake-stored embeddings and external or internal vector indexes as appropriate.
Author Snowpark/Python/SQL transformations, Streams & Tasks, and job orchestration to enable near-real-time and batch analytical workloads.
Implement data modeling and governance patterns within Snowflake: schemas, role-based access control, masking, lineage, and metadata to support analytics and compliance.
Partner with product, ML/AI, BI, and engineering teams to translate business requirements into robust production-ready solutions.
Build monitoring, observability, and cost controls for compute, storage, and API usage related to document analytics and LLM integration.
Produce technical documentation, runbooks, and clear explanations of model/LLM behavior and limitations to non-technical stakeholders.
Analytical Skills
Critical Thinking: Ability to think critically and approach problems from multiple perspectives.
Quantitative Analysis: Strong quantitative skills to interpret and manipulate data effectively.
Attention to Detail: Meticulous attention to detail to ensure accuracy in data analysis and model development.
Interpersonal Skills
Communication: Excellent verbal and written communication skills to convey complex findings to non-technical stakeholders.
Team Collaboration: Ability to work collaboratively with diverse teams to achieve common goals.
Leadership: Capability to lead projects and mentor junior team members.
Education
Education: A bachelor's degree in a related field such as Statistics, Mathematics, Computer Science, or Engineering. Advanced degrees (Master's or Ph.D.) in these fields are highly desirable.
Experience: Relevant experience in data analysis, machine learning, and business intelligence
Pay Range
The typical pay range for this role is:
$118,450.00 - $236,900.00
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 (https://learn.bswift.com/cvshealth-mainland) .
We anticipate the application window for this opening will close on: 07/01/2026
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
CVS Health is an equal opportunity/affirmative action employer, including Disability/Protected Veteran - committed to diversity in the workplace.
