Staff Applied Machine Learning Scientist (Health)

Join WHOOP, a leading health and fitness technology company, as a Staff Applied Machine Learning Scientist on the Health team. In this role, you will develop algorithms and features that enhance WHOOP's health sensing capabilities, focusing on women's health, wellness, and emerging health signals. You will turn ambiguous clinical and product questions into defensible studies, models, and evaluation plans, and set the methodological bar for the Health domain. You will also partner with Machine Learning Engineers and collaborate with Product, clinical, and Digital Health partners.

Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply

About WHOOP

Clear, high-signal communicator who can move between deeply technical method discussions and cross-functional conversations with Product, clinical partners, and leadership

Responsibilities

• Concevoir, développer et valider des méthodes d'apprentissage automatique et statistiques pour les fonctionnalités de santé, en utilisant des données physiologiques et comportementales.
• Transformer des questions cliniques et de produit ambiguës en études, modèles et plans d'évaluation défendables sur les données longitudinales de WHOOP.
• Élever la qualité et la vitesse des scientifiques et des ingénieurs autour de vous grâce à des revues de conception, du mentorat et le jugement de savoir quand coacher quelqu'un d'autre.
• Strong Python skills with reproducible scientific / ML engineering hygiene (clean, reviewable code; comfort with stacks such as NumPy/SciPy/pandas, scikit-learn, PyTorch/TensorFlow)
• 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 otherwise messy real-world data
• Demonstrated Staff-level technical leadership: setting methodological standards others reuse, influencing multi-team outcomes, mentoring senior ICs, and making prioritization calls under uncertainty. End-to-end project ownership alone is not sufficient

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
• 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

Pay

USD 170,000 - 230,000