> Markdown version of [/jobs/ext/3776778-data-scientist-pharmacy-benefits](https://www.wearedevelopers.com/jobs/ext/3776778-data-scientist-pharmacy-benefits). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - Pharmacy Benefits - **Company:** CVS Health - **Location:** Chicago, IL, United States - **Salary:** $79,310.0 - $173,040.0 - **Contract:** Permanent contract - **Skills:** Data Analysis, Big Data, Cloud Computing, Data Mining, Python (Programming Language), Machine Learning, Operational Data Store, Software Deployment, SQL Databases, Google Cloud, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Model Validation, Generative AI, AI Coding Agents, Information Technology, Production Code, Drift Detection, Software Coding, Software Version Control - **Published:** October 16, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=b1ac33231b05c7bf ## About the Role * 1+ years of professional experience writing production-grade code in Python and querying complex datasets using SQL. * 1+ years of professional experience building, validating, and deploying machine learning models, statistical models, algorithmic solutions, or LLM-based solutions. * Experience using Git/GitHub for version control and collaborative code development. * Experience delivering presentations, reports, or business reviews to cross-functional stakeholders., * Initiative: Proven ability to independently investigate ambiguous problems, interview process owners, and structure solutions rather than waiting for defined specifications. * Stakeholder Communication: Ability to translate complex data science concepts into clear business risks and opportunities for non-technical partners. * ML Model & Generative AI: Practical experience building ML solutions or LLM-based solutions (e.g., prompt design, RAG architectures, model evaluation frameworks). * Production Monitoring: Experience establishing model performance guardrails, such as drift detection, production monitoring, and alerting systems. * Domain Experience: Familiarity with healthcare data (e.g., pharmacy claims, clinical workflows, or unstructured clinical notes). * Cloud & Tools: Familiarity with Google Cloud Platform (GCP) or similar enterprise cloud environments, as well as AI-assisted software development tools. Education * Bachelor's degree or equivalent work experience in Computer Science, Statistics, Mathematics, Analytics, Engineering, or a related quantitative field. * Master's degree in a quantitative field or MBA strongly preferred. ## Description 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 motivated data professionals to drive efficiency and growth in Caremark's PBM business, using advanced analytics, predictive modeling, statistics, and generative AI. 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 data scientist role, with a combination of business / stakeholder consulting and data analysis/science. You will work with a squad of 10+ data scientists and engineers to manage the whole lifecycle from ideation to deployment and monitoring. * Partner closely with clinical and operational experts to unpack complex operational processes, identify bottlenecks, and translate requirements into technical solutions. * Design, build, and validate data science solutions-ranging from Generative AI pipelines and predictive models to rules-based logic. * Manage end-to-end delivery from initial data extraction and modeling to production deployment and lifecycle monitoring (e.g., testing, performance dashboards, alert thresholds). * Extract, curate, and analyze large-scale operational data using SQL and Python. * Translate complex modeling outputs into clear, business-driven narratives and presentations for clinical and business leadership.