Head of Data Science & Analytics

Role overview Lead the development of data science and analytics capabilities that inform patient identification, treatment demand, care engagement, and program impact. This hands-on leadership role combines statistical modeling, healthcare analytics, cross-functional planning, and team building, with work spanning both production workflows and the reporting teams use to run the business.

Responsibilities - Build methods to identify people who may benefit from specialty care support using claims, eligibility, clinical, and operational data. - Develop and validate models for engagement, treatment initiation, adherence, and persistence; account for missing data, claims delays, and differences between populations. - Forecast specialty therapy use and treatment volumes, quantifying uncertainty in sparse, variable populations and high-cost cases. - Measure program effects on costs, access, and outcomes using appropriate experimental or observational methods, and communicate limitations clearly. - Set shared metrics, reporting standards, and quality controls with data engineering and analytics partners; investigate changes caused by business shifts, source corrections, or methodology updates. - Lead the analytical roadmap, work with functional leaders to prioritize needs, and hire and develop data scientists or analysts as the capability grows.

Requirements - Experience developing statistical or machine learning methods that inform decisions or enter operational use, including validating and monitoring them. - Strong statistical foundations and practical experience with methods such as forecasting, hierarchical or Bayesian modeling, survival analysis, causal inference, or simulation. - Hands-on experience with large healthcare datasets, including claims or eligibility data, and their coding, timing, and completeness challenges. - Ability to work with business leaders to define outcomes, prioritize analytical work, and deliver models, reporting, and quality controls. - Experience leading data scientists or analysts, or building a substantial analytical capability, while remaining close to the technical work. - Effective use of AI tools alongside independent judgment, reproducible methods, and appropriate validation.

Nice to have Experience in specialty pharmacy, payer, pharmacy benefit management, actuarial, health benefits, or clinical outcomes analytics; longitudinal patient cohort analysis; healthcare cost or adherence evaluation; forecasting rare, high-cost events; or analytical modeling and self-service reporting tools such as dbt, Omni, or Looker. An advanced quantitative degree or equivalent applied experience is also relevant.