How do you identify patients who could benefit from specialty care support within broad, imperfect healthcare datasets? How do you forecast treatment demand across populations where a small number of patients can drive substantial variation? How do you reliably estimate the savings and impact of a care model when claims arrive late, treatment patterns change, and the alternative course of care isn’t always directly observed?

As Head of Data Science & Analytics, you’ll lead Leap’s work on these questions. You’ll develop the methods and models that help us identify patients, anticipate their needs, forecast population behavior, and measure our effect on costs, adherence, and access to care. Your work will directly inform how we engage members, support clients, source therapies, and invest in the business.

Our team has built a solid foundation across data engineering, analytics, and operational reporting. This role builds on that work, bringing dedicated data science and analytical leadership to substantially expand our capabilities. You’ll develop new ways to predict what comes next, evaluate which analytical models are effective, and apply those insights across a rapidly growing business.

You’ll partner directly with leaders across Finance, Commercial Growth, Client Success, Pharmacy, Clinical Operations, and Product to answer complex analytical questions that shape our strategy and strengthen the operational reporting they use to run their teams. Building on our existing infrastructure, you’ll work with data engineering and analytics colleagues to strengthen shared metrics, expand reporting capabilities, and embed quality controls that keep our numbers reliable as Leap scales. You’ll own a cross-functional roadmap that connects these investments to business priorities, giving every team a clear view of performance and the analytical tools to improve it.

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.

Key Responsibilities

Develop Patient Intelligence and Predictive Models

• Develop methods for identifying patients who may benefit from Leap using medical and pharmacy claims, eligibility records, clinical information, and operational data.
• Translate incomplete and fragmented observations into useful signals about therapy utilization, treatment status, care transitions, and opportunities for engagement.
• Develop and validate models of patient engagement, treatment initiation, adherence, and persistence, with clear definitions of what the available data can reliably establish.
• Evaluate models for accuracy, calibration, bias, and operational usefulness, accounting for missing data, claims lag, and differences across populations.
• Partner with Engineering, Product, and Clinical Operations to bring validated methods into production workflows and monitor their performance over time.

Forecast Population Behavior and Demand

• Build population-level models that connect covered lives to specialty therapy utilization, serviceable members, active patients, and treatment volume.
• Account for stochastic variation, sparse observations, population differences, and the outsized effect of individual patients and high-cost therapies.
• Apply 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, distinguishing underlying trends from random variation and changes in data availability.
• Backtest forecasts, investigate deviations, and translate results into decisions about procurement, clinical capacity, client strategy, and financial planning.

Establish Rigorous Methods for Measuring Impact

• Develop defensible methodologies and algorithms for estimating Leap’s savings, patient adherence, access to care, and broader program impact.
• Define appropriate baselines and counterfactuals, accounting for factors such as patient selection, treatment mix, site of care, drug pricing, and observation periods.
• Establish a consistent relationship between pre-sale savings estimates, account forecasts, and realized results, making assumptions and sources of uncertainty explicit.
• Design experiments and observational analyses to understand which engagement and care interventions improve outcomes.
• Partner with Finance, Clinical, and Client Success leaders to make these methods reproducible, explainable, and useful for internal decisions and client reporting.

Partner Across the Business and Scale Operational Reporting

• Work directly with functional leaders to identify their most important data science and operational questions, translating them into a shared roadmap for models, analyses, and reporting.
• Build on Leap’s existing reporting infrastructure to support growing client and patient populations, more complex operations, and new business capabilities.
• Partner with each team to develop reliable views of its performance, including volumes, conversion, capacity, timeliness, quality, and outcomes, with the ability to investigate what drives changes.
• Strengthen shared definitions and calculations for core metrics—including member funnel stages, activation, treatments, adherence, and savings—so company-wide reporting and departmental views remain consistent.
• Expand Omni and the underlying analytical models into a durable self-service environment, making validated metrics accessible across dashboards, ad hoc analyses, and AI-assisted queries.
• Own the reporting lifecycle with data engineering and analytics counterparts: prioritize requirements, define acceptance criteria, validate outputs, support adoption, and maintain reporting as workflows evolve.
• Maintain the business data dictionary and documentation of analytical methods and source lineage, with clear communication when definitions, sources, or methodologies change.

Embed Quality and Reproducibility

• 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 the analytical workflow.
• Make results reproducible through shared calculation logic, version control, documented assumptions, and appropriate peer review.
• Make changes in results understandable, distinguishing business movement from new data, source corrections, or methodological updates.

Lead and Build the Function

• Set the scientific and analytical direction for Leap, balancing foundational capabilities with opportunities to deliver new value.
• Hire, develop, and lead data scientists and analysts as the company grows.
• Work closely with data engineering and product counterparts to translate analytical requirements into durable technical capabilities.
• Communicate complex methods and findings clearly, helping colleagues understand both what the evidence supports and where uncertainty remains.
• Build a culture of curiosity, rigor, and practical experimentation across the company.

Required

• You’ve developed statistical or machine learning methods that informed real decisions or became part of operational workflows, and you’ve owned their validation and ongoing performance.
• You have strong statistical foundations and experience with several relevant methods, such as hierarchical or Bayesian modeling, forecasting, survival analysis, causal inference, or simulation. You know how to choose an approach that fits the data and the decision.
• You’ve worked hands-on with large healthcare datasets, including claims or eligibility data, and understand their coding, timing, completeness, and interpretation challenges.
• You’ve worked with functional leaders to define how outcomes and performance are measured, built a roadmap from their most important questions, and delivered the models, reporting, and quality controls that put it into use.
• You’ve led data scientists or analysts, or built a substantial capability as an early technical hire, and want to remain close to the work.
• You use AI tools effectively while maintaining independent judgment, reproducibility, and appropriate validation.

Preferred

• 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.

At Leap, we’re building an outlier company with real impact — and that takes focus, energy, and commitment. If that excites you, we’d love to hear from you.