Senior Data Scientist - Payment Accuracy

CVS Health•New York, NY

3d•$111,240 - $222,480

About The Position

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. Position Summary It’s a new day in healthcare! Looking for opportunities to use cutting edge technologies analyzing petabytes of data on Google Cloud Platform? Interested in generating analytical insights that inform provider networks and engagement strategies that support members on their individualized health and wellness journey? CVS delivers strategically impactful programs and tools that expand convenient access to high-quality and affordable care for all members across multiple channels across all stages of life. At CVS, we’re pioneering a total approach to health and wellness. As a Sr Data Scientist on the Payment Optimization Analytics team, you’ll build industry-leading analytics, data, and technology platforms to help reimagine health care. This position will play a critical role within a cross-functional team, delivering powerful solutions that inform network strategy and design, improve network performance and provider engagement, develop new provider offerings, and enhance consumer experience. You will be part of a fast-paced, innovative, collaborative team, helping solve problems, drive impact, and deliver key insights to our customers by leveraging innovations in data science, machine & deep learning, and predictive modeling techniques. You will derive meaning from data and deploy robust solutions through provider-directed engagement channels (e.g., digital) that helps consumers identify affordable care, when they need it, and with whom.

Requirements

5 or more years of progressively complex related experience
Experience in SQL (BigQuery, complex CTEs, window functions), Python (pandas, NumPy, scikit-learn), machine learning (model development, feature engineering, evaluation), and AI/LLM integration (prompt engineering, RAG, agent frameworks) for end-to-end data science workflows.
Demonstrates strong ability to communicate technical concepts and implications to business partners.
Anticipates and prevents problems and roadblocks before they occur.
Strong knowledge of advanced analytics tools and languages to analyze large data sets from multiple data sources.
Demonstrates proficiency in most areas of mathematical analysis methods, machine learning, statistical analyses, and predictive modeling and in-depth specialization in some areas.

Nice To Haves

Healthcare industry work experience is preferred

Responsibilities

Leads and participates in the development, validation and delivery of algorithms, statistical models, and reporting tools.
Solves complex analytical problems.
Develops and/or uses algorithms and statistical predictive models and determines analytical approaches and modeling techniques to evaluate scenarios and potential future outcomes.
Performs analyses of structured and unstructured data to solve multiple and/or complex business problems utilizing advanced statistical techniques and mathematical analyses and broad knowledge of the organization and/or industry.
Collaborates with business partners to understand their problems and goals, develop predictive modeling, statistical analysis, data reports and performance metrics.
Develops and participates in presentations and consultations to existing and prospective constituents on analytics results and solutions.
Interacts with internal and external peers and managers to exchange complex information related to areas of specialization.
Use strong knowledge in algorithms and predictive models to investigate problems, detect patterns and recommend solutions.
Use strong programming skills to explore, examine and interpret large volumes of data in various forms.