Senior Data Scientist

CVS Health

Senior Data Scientist leading multiple analytics projects at CVS Health. Focusing on sales and underwriting analytics with responsibilities in mentoring and technical direction.

Posted 7/14/2026full-timeRemote • Florida, Massachusetts • 🇺🇸 United StatesSenior💰 $92,700 - $203,940 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data science with a focus on financial services, utilizing advanced analytical techniques and tools to drive business decisions. Capable of mentoring junior team members and effectively communicating complex findings to senior stakeholders.

Highest-signal resume keywords

Data Science ExperienceProduction-Grade PythonAdvanced SQLML Model Lifecycle FamiliarityExperience Leading Workstreams

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

RegressionClassificationClusteringTime-Series ForecastingExperiment Design

Soft Skills

MentoringCommunication

Tools & Technologies

PySparkDatabricksSnowflakeDistributed Computing

Industry Keywords

Financial ServicesInsuranceHealthcarePBM

Tech Stack

Tools & technologies

PySparkPythonSQL

About the role

Key responsibilities & impact

own multiple concurrent projects across model development, strategic analysis, and analytics product delivery — framing the business question, designing the approach, building the solution, and presenting findings to senior stakeholders (Lead Director, Executive Director, AVP)
set technical direction for your workstream, mentor junior data scientists, and partner directly with business leaders across sales, underwriting, and finance to turn analysis into action

Requirements

What you’ll need

5–8+ years of data science experience, with at least 2 years in financial services, insurance, healthcare, or PBM
Track record of analytical work that changed business decisions — not just answered questions
Production-grade Python and advanced SQL; proficiency with PySpark, Databricks, Snowflake, distributed computing or equivalent.
Strong fundamentals across regression, classification, clustering, time-series forecasting, and experiment design — with the judgment to pick the right tool for each problem
Familiarity with the ML model lifecycle: training, validation, deployment, monitoring, and drift detection
Ability to explain a model's assumptions and limits to a non-technical executive in three sentences
Experience leading workstreams, mentoring peers, and partnering with senior stakeholders (Lead Director and above)

Benefits

Comp & perks

medical, dental, and vision coverage
paid time off
retirement savings options
wellness programs
comprehensive benefits package designed to support the physical, emotional, and financial well-being of colleagues and their families