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Strava is a global fitness community with over 200 million athletes across 185+ countries. The Sport Science team sits at the executive level, reporting directly to the CEO, and is responsible for scientific validation, safety review, and applied research for Strava's and Runna's fitness products.
In this role, you will own two primary responsibilities: (1) ongoing scientific validation and safety review of Runna's and Strava's fitness products, including active academic audits; and (2) the applied research agenda that advances the science of sport and fitness.
Key responsibilities include:
- Lead scientific validation and safety review processes for fitness products, managing open items in active academic audits
- Manage and expand academic partnerships with Stanford University and Loughborough University, and identify new partnership opportunities
- Define data and labeling strategies (perceived exertion, injury history, sleep, etc.) that make AI/ML model insights scientifically defensible
- Partner with Runna's coaching team on recurring scientific review cadences, serving as the authority on disputed questions about science application in coaching
- Build out the Sport Science team and establish an external academic advisory council
- Represent Strava's scientific credibility externally through published research, conference presentations, and media appearances
- Work closely with coaching, AI/ML, Data Science, Product, Public Policy, and Legal teams
The role operates on a flexible hybrid model requiring more than half your time on-site in the San Francisco office, approximately three days per week.
REQUIREMENTS:
- MD, PhD, or equivalent applied track record in exercise physiology, sports science, biomechanics, sports medicine, or closely related field
- Substantial experience combining peer-reviewed research with applied work in industry, elite sport, or research-focused sports medicine settings, with demonstrated ability to translate science into product decisions
- Track record of managing external academic or clinical partnerships, including agreements, multi-institution collaborations, and ethics-related governance
- Plus factors: comfort partnering with data science and ML teams to translate research questions into model and data requirements (coding not required); experience building a team or function from first hire; track record of external scientific representation (papers, conference talks, podcasts); endurance or running-specific research experience
