Senior Data Scientist - Risk Prediction Engine

CVS Health•New York, NY

22h•$111,240 - $222,480

About The Position

CVS Health is seeking a Senior Data Scientist to develop the next generation of healthcare risk prediction models. The role uses large-scale data, industry knowledge, analytical methods, and machine learning to provide insights and model solutions to stakeholders across CVS Health and Aetna. The team tackles diverse use cases and applies emerging machine learning techniques to develop and refine risk models. The position emphasizes problem solving, quantitative analysis, algorithmic judgment, rapid learning in unfamiliar domains, and efficiently testing ideas. It also calls for hands-on use of AI tools to improve productivity, an understanding of how large language models work, and thoughtful incorporation of AI into data science processes. The role develops and applies algorithms and statistical predictive models; analyzes structured and unstructured data; collaborates with business partners on modeling and analytics solutions; and presents results to stakeholders. It also involves investigating problems, identifying patterns, interpreting large volumes of data, and applying sound risk management judgment when developing and validating models used for business-critical decisions. The role uses SQL, Python, Spark, and cloud computing platforms such as AWS, GCP, or Microsoft Azure.

Requirements

Bachelor's degree in Mathematics, Statistics, Computer Science, Business Analytics, Economics, Physics, Engineering, or a related discipline.
At least 3 years of professional experience in data analytics, business intelligence, data engineering, or applied data science.
At least 3 years of experience with SQL and Python or R.
Strong programming skills and experience working with large volumes of data.
Strong algorithmic instincts and quantitative problem-solving skills.
Experience with SQL, Python, Spark, and cloud computing.

Nice To Haves

Master's or PhD degree in Mathematics, Statistics, Computer Science, Business Analytics, Economics, Physics, Engineering, or a related discipline.
At least 5 years of professional experience in data analytics, business intelligence, data engineering, or applied data science.
At least 5 years of experience with SQL and Python or R.
Demonstrated track record of using AI tools to accelerate personal workflow, alongside solid algorithmic and risk management acumen.
Hands-on experience leveraging large language models and AI/ML ecosystem tools, with a working understanding of how these models function.

Responsibilities

Develop and use algorithms and statistical predictive models, selecting analytical approaches and modeling techniques to evaluate scenarios and potential future outcomes.
Analyze structured and unstructured data to solve complex business problems using advanced statistical and mathematical techniques.
Collaborate with business partners to understand problems and goals and develop predictive models, statistical analyses, data reports, and performance metrics.
Prepare and participate in presentations and consultations about analytics results and solutions for existing and prospective constituents.
Exchange complex information related to areas of specialization with internal and external peers and managers.
Use algorithms and predictive models to investigate problems, detect patterns, and recommend solutions.
Use programming skills to explore, examine, and interpret large volumes of data in different forms.
Apply sound risk management judgment when developing and validating models that inform business-critical decisions.
Ramp up quickly on unfamiliar domains and methods, form hypotheses, and test them efficiently.
Incorporate AI tools and large language models into data science processes to improve productivity, efficiency, insight, and innovation.