Lead Data Scientist, Experimentation Platform

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. Start your journey https://www.strava.com/subscription 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. This is a strategic individual contributor role that will partner closely with our engineering and data teams to deliver improvements to our experimentation platform for product and marketing improvements. The role drives organization-wide through expanding functionality of and visibility into our experiments, and by partnering with cross-functional teams to define processes that enable Strava to deliver the highest quality experiences to our athletes. 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: - Build industry-leading experimentation process and platform for a consumer product serving ~tens of millions of athletes worldwide. - Partner with engineers, product managers, and marketers to ensure experiments are successfully designed and shipped across product surfaces and marketing channels - Collaborate closely with our Comms & Experimentation Platform engineers to expand our technical capabilities to expand our

Department: Department; Team: Data and Insights