Head of Data Science & Analytics

Leap

1d•$200,000 - $250,000

About The Position

Leap is hiring a hands-on data science and analytics leader to develop methods and models that identify patients who may benefit from specialty care support, anticipate treatment needs, forecast population behavior, and measure the effects of Leap’s care model on costs, adherence, and access. The role builds on existing data engineering, analytics, and operational reporting capabilities, expanding data science and analytical leadership across the business. The Head of Data Science & Analytics will partner with leaders in Finance, Commercial Growth, Client Success, Pharmacy, Clinical Operations, Product, and Engineering. The role owns a cross-functional roadmap connecting analytical investments to business priorities, strengthens shared metrics and reporting, and embeds quality controls as the company grows. This is a hands-on leadership role with the opportunity to build a team and shape the function. It reports to Leap’s President and co-founder.

Requirements

Has 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; able to choose approaches suited to the data and decision.
Hands-on experience with large healthcare datasets, including claims or eligibility data, and understanding of their coding, timing, completeness, and interpretation challenges.
Experience working with functional leaders to define outcome and performance measures, build a roadmap from their key questions, and deliver models, reporting, and quality controls for use.
Has led data scientists or analysts, or built a substantial capability as an early technical hire, and wants to remain close to the work.
Uses AI tools effectively while maintaining independent judgment, reproducibility, and appropriate validation.

Nice To Haves

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.

Responsibilities

Develop methods to identify patients who may benefit from Leap using medical and pharmacy claims, eligibility records, clinical information, and operational data.
Translate incomplete and fragmented data into useful signals about therapy utilization, treatment status, care transitions, and engagement opportunities.
Develop and validate models of patient engagement, treatment initiation, adherence, and persistence, and clearly define what available data can establish.
Evaluate models for accuracy, calibration, bias, and operational usefulness, accounting for missing data, claims lag, and population differences.
Partner with Engineering, Product, and Clinical Operations to deploy validated methods in production workflows and monitor ongoing performance.
Build population-level models connecting covered lives to specialty therapy utilization, serviceable members, active patients, and treatment volume.
Use appropriate statistical methods, such as hierarchical models, time-to-event analysis, and simulation, to estimate outcomes and quantify uncertainty.
Forecast performance by client, therapy, and time period; backtest forecasts; investigate deviations; and inform procurement, clinical capacity, client strategy, and financial planning.
Develop defensible methods for estimating savings, adherence, access to care, and broader program impact.
Define baselines and counterfactuals that account for patient selection, treatment mix, site of care, drug pricing, and observation periods.
Connect pre-sale savings estimates, account forecasts, and realized results, making assumptions and uncertainty explicit.
Design experiments and observational analyses to assess the effects of engagement and care interventions.
Work with Finance, Clinical, and Client Success leaders to make impact-measurement methods reproducible, explainable, and useful for internal decisions and client reporting.
Partner with functional leaders to identify important analytical and operational questions and turn them into a shared roadmap for models, analyses, and reporting.
Expand reporting capabilities for growing client and patient populations, more complex operations, and new business capabilities.
Develop reliable performance views for each team, including volumes, conversion, capacity, timeliness, quality, and outcomes, with ways to investigate changes.
Strengthen consistent company-wide definitions and calculations for core metrics, including member funnel stages, activation, treatments, adherence, and savings.
Expand Omni and underlying analytical models into a self-service environment that makes validated metrics available through dashboards, ad hoc analyses, and AI-assisted queries.
Own the reporting lifecycle with data engineering and analytics counterparts, including prioritizing requirements, defining acceptance criteria, validating outputs, supporting adoption, and maintaining reporting as workflows evolve.
Maintain the business data dictionary and documentation of analytical methods and source lineage, and communicate changes to definitions, sources, or methodologies.
Establish standards for reviewing, testing, and validating models, analyses, and recurring reporting.
Partner with Engineering on checks for data completeness, freshness, identity matching, and reconciliation across systems.
Ensure important patient and operational events are captured and represented consistently throughout analytical workflows.
Make results reproducible through shared calculation logic, version control, documented assumptions, and appropriate peer review.
Explain changes in results by distinguishing business movement from new data, source corrections, or methodological updates.
Set Leap’s scientific and analytical direction, balancing foundational capabilities with opportunities to deliver value.
Hire, develop, and lead data scientists and analysts as the company grows.
Work with data engineering and product counterparts to translate analytical requirements into durable technical capabilities.
Communicate complex methods and findings clearly, including what evidence supports and where uncertainty remains.
Build a culture of curiosity, rigor, and practical experimentation across the company.