Senior Manager, ML Health - Insights

About the Role

As a Senior Manager, Machine Learning on the Health Insights team, you will own delivery and people outcomes across a mixed team of scientists and engineers. You will translate department strategy into clear quarterly plans, develop senior ICs (and, as the team grows, the next layer of managers), hire against a high bar for both crafts, and partner with Product and adjacent Health partners on what is worth building and what the data can support. You will stay close enough to the ML lifecycle to evaluate the team's most important decisions without becoming the default contributor.

Responsibilities

• Own delivery, team health, and people outcomes across Health ML workstreams on a mixed Applied ML Scientist and ML Engineer team.
• Translate department strategy into quarterly plans, milestones, and success criteria; keep the team on the highest-leverage work and adapt deliberately as priorities shift.
• Build the team: lead hiring across science and engineering roles, calibrate the bar for each craft, and shape onboarding, leveling, and growth practices.
• Develop the next layer of leadership: coach senior ICs (and managers, as the scope grows), manage performance with clarity and care, and create the conditions for people to operate above their level.
• Set the standard for how Health ML gets built: work quality, evaluation rigor, review practices, and an operating model that holds as people and workstreams change.
• Partner directly with Product (and with clinical, software, and Digital Health partners as the work requires) to align on roadmap, evidence, and sequencing; broker trade-offs between iteration speed, scientific integrity, and member value.
• Own the risk posture for the team: anticipate execution, technical, and people risks, design mitigation into how the team operates, and keep senior leadership clearly informed of the most consequential decisions.
• Define and own the operating model: planning cadences, design reviews, decision forums, and quality gates appropriate to member-facing health ML.
• Build and continuously raise AI-enabled workflows that create measurable leverage across the team, for development, evaluation, documentation, and stakeholder communication.
• Represent the team's work credibly to executive and cross-functional audiences with brevity, evidence, and clarity.

Qualifications

• 7+ years of experience in machine learning, applied science, or software engineering, with 3+ years managing engineering and/or science teams.
• Bachelor's degree in Computer Science, Engineering, Applied Math, Biomedical Engineering, or a related field; advanced degree preferred.
• Demonstrated track record managing highly senior ICs: hiring, performance management, and developing people who in turn raise the bar around them.
• Deep familiarity with the ML development lifecycle (data collection, model training, evaluation, validation, deployment, and monitoring), sufficient to evaluate the most important technical and methodological decisions the team makes.
• Experience shipping algorithms or ML-enabled product in health, wearables, digital health, or a closely related applied domain.
• Demonstrated ability to set direction and make timely prioritization calls under uncertainty, keeping team focused on the highest-leverage work.
• Demonstrated AI-tooling sophistication: a track record of building or adopting AI-enabled workflows that materially improved team speed, quality, or decision confidence.
• Clear, high-signal communicator who can move fluidly between technical ML discussions and executive-level summaries of risk, trade-offs, and direction.
• Depth in a Health domain WHOOP already works in preferred (member insights, women's health, cardiometabolic or respiratory health, sleep, strength / healthspan, neuroscience, or related physiological time-series work).
• Preferred experience as a counterpart to Product on ambiguous health or member-insight problems: scoping, sequencing, and pushing back when a claim is not supportable.
• Familiarity with clinical validation, evidence packages, or Digital Health partnership on higher-stakes health features preferred.