Staff Applied Machine Learning Scientist (Health)

WHOOP is on a mission to unlock and inspire performance for life. Its wearable technology collects physiological data, providing members with actionable insights into recovery, training, and sleep. The Health team develops the algorithms and features that expand WHOOP's health sensing capabilities, spanning women's health, software as a medical device, wellness and longevity, member insights, and emerging health signals. This role is based in the WHOOP office in Boston, MA, and the successful candidate must be prepared to relocate if necessary.

Responsibilities

Lead the design, development, and validation of machine learning and statistical methods for Health features, using wearable physiological and behavioral data and related member sources; Frame ambiguous health and product questions into well-scoped modeling or study problems, with clear performance criteria, evaluation plans, and honest treatment of uncertainty and failure modes; Set the methodological and quality standard for a Health domain: reusable evaluation practices, review expectations, and documentation that others can inherit; Develop and refine approaches for time-series, longitudinal, and multimodal data, including data curation, labeling strategy, feature and representation choices, and offline evaluation under real-world noise, missingness, and population shift; Partner with Machine Learning Engineers to translate validated methods into production-ready systems, accounting for scalability, reliability, latency, cost, monitoring, and controlled-release constraints; Serve as technical counterpart to Product and Digital Health (clinical science, regulatory, quality): pressure-test performance requirements, study design, and evidence quality, and push back when a claim is not supportable; Raise the quality and velocity of the scientists and engineers around you through design reviews, mentorship, and coaching; Communicate trade-offs, risks, and results clearly to technical, clinical, product, and leadership audiences, and drive decisions with evidence

Requirements

Master's or PhD (or equivalent professional experience) in Biomedical Engineering, Electrical Engineering, Computer Science, Applied Mathematics, Biostatistics, Bioinformatics, Physiology, or a related quantitative or health-science field; 7+ years of professional experience developing algorithms, applied ML models, or statistically rigorous analyses in health, wearables, medical devices, digital health, or a closely related domain; Demonstrated Staff-level technical leadership: setting methodological standards others reuse, influencing multi-team outcomes, mentoring senior ICs, and making prioritization calls under uncertainty; Strong foundation in statistics (Bayesian and frequentist) and machine learning for time-series and longitudinal data, with the ability to draw defensible conclusions from observational, noisy, or messy real-world data; Experience with wearable or physiological signals (PPG, IMU, HR/HRV, sleep, activity, temperature, or similar) or a closely related adjacent time-series domain; Hands-on experience designing studies or evaluation protocols end-to-end: data curation, metric choice, validation splits, subgroup and failure analysis, and documentation of assumptions and limitations; Strong Python skills with reproducible scientific/ML engineering hygiene (NumPy/SciPy/pandas, scikit-learn, PyTorch/TensorFlow); Clear, high-signal communicator who can move between deeply technical method discussions and cross-functional conversations with Product, clinical partners, and leadership