Head of Data Science – Analytics

This is a fully remote position, open to applicants in United States.

📋 Description

• Lead Leap’s data science and analytics initiatives to identify patients who could benefit from specialty care support.

• Create methodologies and predictive models utilizing medical and pharmacy claims, eligibility records, clinical data, and operational metrics.

• Convert incomplete and fragmented observations into insights regarding therapy utilization, treatment status, care transitions, and engagement opportunities.

• Develop and validate models focused on patient engagement, treatment initiation, adherence, and persistence.

• Assess model accuracy, calibration, bias, and operational applicability.

• Collaborate with Engineering, Product, and Clinical Operations to implement validated methods and track performance.

• Construct population-level models that link covered lives to specialty therapy utilization, serviceable members, active patients, and treatment volume.

• Utilize hierarchical models, time-to-event analysis, simulation, and other statistical methods to quantify uncertainty.

• Predict performance by client, therapy, and time period; backtest forecasts and analyze deviations.

• Convert forecasts into procurement, clinical capacity, client strategy, and financial planning decisions.

• Create methodologies for estimating savings, adherence, access to care, and broader program impacts.

• Establish baselines and counterfactuals while considering selection, treatment mix, site of care, pricing, and observation periods.

• Design experiments and observational analyses for engagement and care interventions.

• Collaborate with Finance, Clinical, and Client Success leaders on reproducible and explainable methods and reporting.

• Develop a cross-functional roadmap for models, analyses, operational reporting, and quality controls.

• Enhance shared metrics, reporting infrastructure, data definitions, and analytical models.

• Expand Omni and underlying analytical models into a self-service platform for dashboards, ad hoc analyses, and AI-assisted queries.

• Oversee the reporting lifecycle, including requirements, acceptance criteria, output validation, adoption, and maintenance.

• Maintain the business data dictionary and document methods and source lineage.

• Set standards for reviewing, testing, and validating models, analyses, and recurring reporting.

• Collaborate with Engineering on data completeness, freshness, identity matching, and reconciliation checks.

• Ensure that significant patient and operational events are consistently included in analytical workflows.

• Make results reproducible through shared logic, version control, documented assumptions, and peer review.

• Lead the scientific and analytical strategy for Leap.

• Recruit, develop, and lead data scientists and analysts.

• Clearly communicate complex methods and findings.

• Foster a culture of curiosity, rigor, and practical experimentation.

• Report directly to Leap’s President and co-founder.

⛳️ Requirements

• Developed statistical or machine learning methodologies that informed real-world decisions or became integrated into operational workflows, including validation and ongoing performance management.

• Strong statistical foundation with experience in hierarchical or Bayesian modeling, forecasting, survival analysis, causal inference, or simulation.

• Hands-on experience with large healthcare datasets, particularly claims or eligibility data.

• Understanding of healthcare data coding, timing, completeness, and interpretation challenges.

• Experience collaborating with functional leaders to define outcome and performance measurement criteria.

• Experience in building roadmaps and delivering models, reporting, and quality controls.

• Led data scientists or analysts, or developed substantial capability as an early technical hire.

• Effective use of AI tools with sound judgment, reproducibility, and proper validation practices.

• Preferred: experience in specialty pharmacy, payer, PBM, actuarial, health benefits, or clinical outcomes analytics.

• Preferred: experience identifying patient cohorts or treatment patterns from longitudinal healthcare data.

• Preferred: experience measuring healthcare costs, adherence, or program impact using experimental or observational methods.

• Preferred: experience forecasting low-frequency, high-cost events or heterogeneous populations with limited historical data.

• Preferred: experience with analytical data modeling, semantic layers, and self-service reporting tools such as dbt, Omni, or Looker.

• Preferred: advanced degree in statistics, biostatistics, computer science, applied mathematics, economics, epidemiology, or a related quantitative field, or equivalent practical experience.

• Preferred: experience building in a fast-growing company.

• United States work authorization and visa sponsorship information requested in the application.

🏝️ Benefits

• Equity / stock options.

• Competitive total rewards package.

• Comprehensive benefits.

• Up to 3 applications allowed in any 90-day period.

• Ability to reapply for the same role after 60 days if not offered the position.