Head of Data Science & Analytics

As Head of Data Science & Analytics, you'll lead Leap's work on identifying patients, forecasting population behavior, and measuring the impact of care models on costs, adherence, and access to care. This is a hands-on leadership role with room to build a team and shape the function. It reports to Leap's President and co-founder.

Responsibilities

Develop methods for identifying patients who may benefit from Leap using medical and pharmacy claims, eligibility records, clinical information, and operational data; Develop and validate models of patient engagement, treatment initiation, adherence, and persistence; Evaluate models for accuracy, calibration, bias, and operational usefulness; Partner with Engineering, Product, and Clinical Operations to bring validated methods into production workflows; Build population-level models that connect covered lives to specialty therapy utilization, serviceable members, active patients, and treatment volume; Forecast performance by client, therapy, and time period; Backtest forecasts, investigate deviations, and translate results into decisions about procurement, clinical capacity, client strategy, and financial planning; Develop defensible methodologies and algorithms for estimating savings, patient adherence, access to care, and broader program impact; Define appropriate baselines and counterfactuals; Design experiments and observational analyses to understand which engagement and care interventions improve outcomes; Partner with functional leaders to translate their questions into a shared roadmap for models, analyses, and reporting; Strengthen shared definitions and calculations for core metrics; Expand Omni and the underlying analytical models into a durable self-service environment; Own the reporting lifecycle with data engineering and analytics counterparts; Maintain the business data dictionary and documentation of analytical methods and source lineage; Establish standards for reviewing, testing, and validating models, analyses, and recurring reporting; Set the scientific and analytical direction for the company; Hire, develop, and lead data scientists and analysts; Communicate complex methods and findings clearly; Build a culture of curiosity, rigor, and practical experimentation

Requirements

Developed statistical or machine learning methods that informed real decisions or became part of operational workflows, and owned their validation and ongoing performance; Strong statistical foundations and experience with several relevant methods, such as hierarchical or Bayesian modeling, forecasting, survival analysis, causal inference, or simulation; Hands-on experience with large healthcare datasets, including claims or eligibility data, and understanding of their coding, timing, completeness, and interpretation challenges; Worked with functional leaders to define how outcomes and performance are measured, built a roadmap from their most important questions, and delivered models, reporting, and quality controls; Led data scientists or analysts, or built a substantial capability as an early technical hire; Uses AI tools effectively while maintaining independent judgment, reproducibility, and appropriate validation

Nice to have

Experience in specialty pharmacy, payer, PBM, actuarial, health benefits, or clinical outcomes analytics; Experience identifying patient cohorts or treatment patterns from longitudinal healthcare data; Experience measuring healthcare costs, adherence, or program impact using experimental or observational methods; Experience forecasting low-frequency, high-cost events or heterogeneous populations with limited historical data; Experience with analytical data modeling, semantic layers, and self-service reporting tools such as dbt, Omni, or Looker; An advanced degree in statistics, biostatistics, computer science, applied mathematics, economics, epidemiology, or a related quantitative field, or equivalent applied experience; Experience building in a fast-growing company