Data Scientist, Pharmacy (Part D Stars) at Alignment Health in Anywhere in the U.S.

Job description

• As a Data Scientist on the Pharmacy team, you’ll play a key role in evolving the analytical engine behind our pharmacy data and Part D Star measures within our Medicare Advantage line of business. This role focuses on applied data science to enhance the analytics, trending, and predictive modeling that inform pharmacy and Part D Star strategy. You'll help make tools more transparent, precise, adaptable, and actionable — incorpo-rating new methods, partnerships, and technologies along the way.

Responsibilities

• Partner with Pharmacy leadership to understand business problems, propose analytical solutions, and turn findings into clear, actionable takeaways for stakeholders.
• Convert analytical models and business rules into scalable, production-ready tools.
• Design dashboards and reporting that track Stars measure trajectories and intervention impact.
• Build cut-point projection and simulation models to forecast Star Rating outcomes under varying performance and CMS methodology scenarios.
• Build out prioritization rules, measure improvement models, and next-best-action (NBA) logic.
• Analyze the financial impact of pharmacy and Star performance to help leadership prioritize investment and interventions.
• Manage projects end to end – from problem definition through analysis, model building, validation, deployment support, and ongoing monitoring.
• Collaborate with data and engineering teams to version, test, and deploy models using Git, CI/CD pipelines, and virtual machine (VM) environments.
• Help standardize definitions, documentation logic, and reporting workflows to scale enterprise-wide AI-readiness.
• Bring in new data sources, techniques, and emerging tools to continuously improve analytical capabilities.
• Other duties as assigned by your manager.
• Supervisory Responsibilities: N/A

Requirements

• To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
• 2+ years of relevant experience in predictive modeling and data analysis
• Bachelor or Master’s in Computer Science, Engineering, Mathematics, Statistics, or related field (PhD a plus), or equivalent related experience.
• Excellent communication, analytical and collaborative problem-solving skills.
• Experience in building data science solutions and applying machine learning methods to real world problems with measurable outcomes.
• Familiarity with time-series analysis or forecasting methods.
• Solid data structures & algorithms background.
• Strong programming skills in one of the following: Python, Java, R, Scala or C++
• Demonstrated proficiency in SQL and relational databases.
• Experience with data visualization and presentation, turning complex analysis into insight.
• Experience in setting experimental analytics frameworks or strategies for complex scenarios.
• Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development, and evaluation data sets, etc.
• Experience with manipulating and analyzing complex, high-volume, high-dimensionality and unstructured data from varying sources
• Prior healthcare experience, ideally supporting Stars or Part D analytics.
• Experience in Big Data processing technologies: Hadoop, Spark, Cosmos.
• Experience in Azure, AWS or other cloud ecosystems.
• Strong understanding of CMS Stars rating measures and methodology.
• Published work in academic conferences or industry circles.
• Demonstrated track record in navigating ambiguity, shifting priorities, and delivering results in an agile environment.

Skills

Required

• Python
• Java
• R
• Scala
• C++
• SQL
• relational databases
• data visualization
• time-series analysis
• forecasting methods
• data structures & algorithms
• machine learning

Benefits

• Pay range may be based on a number of factors including market location, education, responsibilities, experience, etc.