Define and lead WHOOP’s HEOR strategy, aligning economic and outcomes evidence generation with regulated product pipelines, enterprise initiatives, and expansion.
Leverage WHOOP’s existing longitudinal member dataset to design and execute real-world evidence analyses demonstrating health impact, productivity gains, utilization reduction, or risk stratification value.
Design and build cost-effectiveness, budget impact, and ROI models that quantify the economic impact of WHOOP’s health features for employers, payers, and government stakeholders.
Develop real-world evidence (RWE) frameworks leveraging internal and external datasets to assess longitudinal health, utilization, and productivity outcomes.
Analyze large-scale datasets (including wearable-derived biomarkers and engagement metrics) to evaluate population-level impact and value generation.
Lead the development of enterprise-ready value dossiers and structured evidence packages to support commercial engagements and government contracting.
Collaborate cross-functionally with Clinical Science, Regulatory Affairs, Data Science, Product, Legal, and Commercial teams to ensure economic claims are scientifically sound and compliant.
Partner with Clinical Science to embed economically meaningful endpoints into validation studies, ensuring that data collection supports downstream value modeling and enterprise use cases.
Establish processes for documenting assumptions, modeling logic, and version control to ensure defensibility and audit-readiness of economic analyses.
Master’s or PhD in Health Economics, Outcomes Research, Public Health, Health Policy, Biostatistics, Epidemiology, or related quantitative field.
8+ years of experience in HEOR, real-world evidence, or health economics within digital health, medical devices, diagnostics, or healthcare technology.
Demonstrated experience building cost-effectiveness, budget impact, or ROI models (hands-on modeling experience required).
Experience integrating economic endpoints into clinical study design and collaborating with clinical research teams.
Experience supporting enterprise, payer, or government-facing evidence discussions.
Strong quantitative skills, including experience working with large healthcare or real-world datasets.
Proven ability to operate cross-functionally in fast-paced environments involving product, engineering, and clinical stakeholders.
Experience working with wearable-derived data, digital biomarkers, or decentralized/remote health technologies preferred.
Experience supporting Software as a Medical Device (SaMD) programs or regulated product environments preferred.
Proficiency in statistical and modeling tools (e.g., R, Python, SAS, or similar) and experience querying modern data warehouses (e.g., Snowflake or equivalent) preferred.
Experience publishing peer-reviewed economic or real-world evidence research preferred.
Experience building HEOR capability within a scaling organization preferred.
