Lead Data Scientist, Data Products

About Strava

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. No matter your sport or how you track it, Strava’s got you covered. Find your crew, crush your goals, and make every effort count. with Strava today.

Our mission is simple: to motivate people to live their best active lives. We believe in the power of movement to connect and drive people forward.

About This Role

The Data Science team at Strava works across the organization to find solutions to the highest-leverage, and often the most challenging, problems facing the business. We use machine learning, causal inference, and measurement systems to synthesize Strava’s unique data assets into models, metrics, and recommendations that our leadership team can act on with confidence.

We are looking for a Data Scientist to join the Data Products team at Strava, a team at the core of Strava’s AI strategy, responsible for turning Strava's unique community and activity data into reliable, reusable, enriched datasets powering user experiences s is a strategic individual contributor role that will partner closely with a growing team of Machine Learning Engineers. You'll drive the team’s measurement strategy and define the development feedback loop— defining evaluation standards, surfacing where performance is breaking down, and steering the team toward the highest-impact opportunities.

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.

What You’ll Do:

• 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.

What You’ll Bring to the Team:

• 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.

For information on benefits, please .

Movement brings us together. At Strava, we’re building the world’s largest community of active people, helping them stay motivated and achieve their goals.

Our global team is passionate about making movement fun, meaningful, and accessible to everyone. Whether you’re shaping the technology, growing our community, or driving innovation, your work at Strava makes an impact.

When you join Strava, you’re not just joining a company—you’re joining a movement. If you’re ready to bring your energy, ideas, and drive, let’s build something incredible together.

Strava builds software that makes the best part of our athletes’ days even better. Just as we’re deeply committed to unlocking their potential, we’re dedicated to providing a world-class, inclusive workplace where our employees can grow and thrive, too. We’re backed by Sequoia Capital, TCV, Madrone Partners and Jackson Square Ventures, and we’re expanding in order to exceed the needs…