Staff Applied Machine Learning Scientist (Health)

WHOOP; Boston (MA); On-site; USD 180,000 - 260,000; Full time

Job summary

WHOOP in Boston, MA is seeking a Staff Applied Machine Learning Scientist for the Health team. You will turn ambiguous clinical and product questions into defensible studies, models, and evaluation plans using longitudinal wearable and member data, setting the methodological bar for the Health domain.

You will partner with ML Engineers, Product, clinical science, and leadership to determine what data can support and to raise the quality and velocity of the team with independently led domain work.

Qualifications

• Master's or PhD (or equivalent) in a quantitative/health-science field.
• 7+ years developing algorithms or ML analyses in health or wearables.
• Staff-level technical leadership and end-to-end project ownership experience.
• Strong foundation in statistics and ML for time-series/longitudinal data.
• Experience with wearable or physiological signals.
• Hands-on study design and evaluation protocol experience.
• Strong Python skills with reproducible ML engineering practices.
• Clear communicator across technical and non-technical teams.

Responsibilities

• Lead design, development, and validation of ML methods for Health features using wearable data.
• Frame health/product questions into well-scoped modeling problems with evaluation plans.
• Set methodological standards for the Health domain and documentation.
• Develop approaches for time-series and multimodal data; data curation and labeling strategies.
• Partner with ML Engineers to translate methods into production-ready systems.
• Serve as technical counterpart to Product and Digital Health; challenge unsupported claims.
• Raise quality and velocity through reviews, mentorship, and coaching.
• Communicate trade-offs and results to technical, clinical, product, and leadership audiences.

Skills

Staff-level leadership

Python for ML

Time-series ML

Statistics fundamentals

Wearable signals

Study design

Cross-functional communication

Education

Master's or PhD in a quantitative/health field

Tools

NumPy/SciPy

pandas

scikit-learn

PyTorch

TensorFlow

Job description

At WHOOP, we’re on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable technology collects rich physiological data, providing members with actionable insights into their recovery, training, and sleep.

The Health team develops the algorithms and features that expand WHOOP's health sensing capabilities. The work spans women's health, software as a medical device, wellness and longevity, member insights, and emerging health signals. We combine continuous physiological data with clinical research and domain expertise to ship features that are scientifically grounded and useful to millions of members.

As a Staff Applied Machine Learning Scientist on the Health team, you will turn ambiguous clinical and product questions into defensible studies, models, and evaluation plans on WHOOP's longitudinal wearable and member data (physiological signals, behavioral logs, labs and integrations, and related streams). You will set the methodological bar for a Health domain. You will partner with Machine Learning Engineers on the path to production, and with Product, clinical, and Digital Health partners on what the data can and cannot support.

Success in this role requires applied algorithm and statistical craft, the judgment to operate across Health problem types, and Staff-level leadership: launching domain work independently, raising the quality and velocity of the people around you, and making clear prioritization calls under uncertainty.

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 that holds up under real-world noise, missingness, and population shift.

• Partner with Machine Learning Engineers to translate validated methods into production-ready systems, making modeling choices that account for scalability, reliability, latency, cost, monitoring, and (where relevant) controlled-release constraints.

• Serve as a 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 the judgment of when to coach someone else to champion a problem versus taking it yourself.

• Communicate trade-offs, risks, and results clearly to technical, clinical, product, and leadership audiences; prevent misalignment early; drive decisions with evidence and a clear point of view on domain direction.

Qualifications

• 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. End-to-end project ownership alone is not sufficient.

• 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.

• 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 (clean, reviewable code; comfort with stacks such as 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.

This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.

WHOOP is an Equal Opportunity Employer and participates in E-verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.