Senior Data Scientist - PBM Analytics

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.

Within the data science department (“Analytics & Behavior Change”), you’ll have the opportunity to drive the next level of PBM (Pharmacy Benefit Manager) member experience and service quality at a leading national healthcare brand.

We’re looking for data scientists who thrive in ambiguous, fast-moving environments to drive efficiency and growth in Caremark's PBM business, using advanced analytics, predictive modeling, and statistics. We expect the candidate to leverage business acumen and good data storytelling to help Caremark improve member well-being and its product and services. PBM Analytics is one of the key teams within the Analytics & Behavior Change department and partners with the PBM leadership to drive innovation and develop cutting edge solutions.

 

This is a hands-on Senior data scientist role, with a combination of business / stakeholder consulting and, data analysis (technical coding, including using machine learning methods).

Position Summary

• Generate insights, build machine learning, deep learning and statistical predictive models and develop analytical approaches which form the foundation for driving patient engagement tactics aimed at improving medication adherence and patient experience.

• Deploy large scale machine learning and deep learning models in a production environment.

• Effectively collaborate cross functionally with Data Engineering, IT and other technical teams to onboard new data sources, create feature stores and optimize/ automate model development and deployment processes (Github, MLOps etc.)

• Perform data quality assessments, remediate or curate data as necessary, perform data modeling

• Write complex and efficient SQL code and leverage Exploratory Data Analysis techniques to develop insights from millions of adjudicated prescription claims and member level records across the PBM

• Communicate complex modeling outputs into clear business narratives for non-technical audiences & senior leadership to build credibility across levels and functions

• Prepare communication material such as presentations and reports, collaborate effectively with business, marketing, trade and other stakeholders across the organization. 

• Mentor peers and lead intern projects.

Required Qualifications

• 3+ years of hands-on experience in generating business insights, machine learning, and deep learning frameworks 

• Proficiency in evaluating AI outputs for accuracy, mitigating bias, and directing large language models to deliver actionable business insights

• Strong experience with deployment of machine learning and deep learning models in production 

• Strong proficiency with Python and SQL, data quality assessment, and data modeling

• Strong proficiency with Github and MLOps

Preferred Qualifications

• Strong experience with cloud-based ML frameworks (either GCP, AWS, or Azure) 

• Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and deploying machine learning pipelines

• Experience in healthcare and/ or pharma

Education

• Bachelor's degree or equivalent work experience in Mathematics, Statistics, Computer Science, Business Analytics, Economics, Physics, Engineering, or related discipline.

• Master's degree or PhD preferred

Anticipated Weekly Hours

40

Time Type

Full time

Pay Range

The typical pay range for this role is:

$101,970.00 - $222,480.00

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.