Senior Data Scientist — AI-Driven Risk Prediction

Cvshealth; New York (NY); On-site; USD 111,000 - 222,000; Full time

Benefits offered by this job

Medical

Dental

Vision

Paid time off

Retirement plan

Wellness programs

Other resources

Job summary

CVS Health is seeking a data scientist to build predictive risk models and deliver data-driven insights across CVS Health and Aetna. You will leverage SQL, Python, Spark, and cloud platforms to handle large datasets and complex problem spaces.

The role emphasizes hands-on AI/ML tooling usage, collaboration with cross-functional teams, and applying rigorous risk-aware modeling to business decisions. A strong foundation in statistics and machine learning is required.

Qualifications

• Bachelor's degree required in a quantitative field; relevant coursework accepted.
• 3+ years of professional experience in data analytics, BI, data engineering or applied data science.
• 3+ years of experience with SQL and Python/R.

Responsibilities

• Develops and/or uses algorithms and statistical predictive models to evaluate scenarios and outcomes.
• Performs analyses of structured and unstructured data using advanced statistical techniques.
• Collaborates with business partners to understand problems and develop predictive models and metrics.
• Presents analytics results and solutions to stakeholders.

Skills

SQL

Python

Spark

Cloud computing

AI tools

Large language models

Data analysis

Machine learning

Education

Bachelor's degree in Mathematics, Statistics, Computer Science, Business Analytics, Economics, Physics, Engineering, or related discipline

Master's or PhD degree in Mathematics, Statistics, Computer Science, Business Analytics, Economics, Physics, Engineering, or related discipline

Tools

AWS

GCP

Azure

Apache Spark

Job description

CVS Health is seeking a data scientist to build predictive risk models and deliver data-driven insights across CVS Health and Aetna. You will leverage SQL, Python, Spark, and cloud platforms to handle large datasets and complex problem spaces.

The role emphasizes hands-on AI/ML tooling usage, collaboration with cross-functional teams, and applying rigorous risk-aware modeling to business decisions. A strong foundation in statistics and machine learning is required.