About the company
Strava is the app for active people. With over 200 million athletes in more than 185 countries, it’s more than tracking workouts—it’s where people make progress together, from new habits to new personal bests. Our mission is simple: to motivate people to live their best active lives.
Responsibilities
- Define what "good" looks like for Strava's internal models and the ML products built on top of it, setting the evaluation frameworks, offline and online metrics, and quality bars the team steers by.
- Build the measurement layer for cross-domain ML products, scaling the org’s visibility into performance across the business
- Own experimentation and monitoring for production models and contribute to monitoring of key metrics and drift detection for enriched datasets, ensuring quality for downstream athlete-facing experiences.
- Identify and size AI/ML product opportunities, translating ambiguous problem spaces into scoped initiatives with defined success criteria.
- Serve as the data science domain expert for the team, raising the standard for baselines, validation, and evidence quality across ML engineers and cross-functional partners.
Requirements
- 5+ years of experience in data science or a related quantitative domain, including hands-on ownership of model evaluation and measurement for systems running in production.
- Experience defining evaluation frameworks for ambiguous ML problems, including baseline selection, offline and online metric design, and validation strategies where ground truth is imperfect
- Strong SQL proficiency and comfort writing production-quality Python for statistical data processing.
- Fluency in metrics and measurement for consumer software products, and comfort working with cross-functional partners to translate business needs into technical plans and vice versa.
Conditions
- We follow a flexible hybrid model that translates to more than half of your time on-site in our San Francisco office — three days per week.
- For information on benefits, please click here.