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 works to link the efforts of our product and marketing teams to tangible outcomes for our athletes and for our business. You’ll develop novel scientific approaches to measuring out business, developing the necessary causal frameworks and models, and help teams develop and evolve our metric strategy to ensure development across the company points toward the highest-leverage outcomes

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:

• Design measurement strategies for Strava's most complex initiatives, applying experimental and quasi-experimental methods (geo testing, difference-in-differences, IV, synthetic control, etc..) to quantify business outcomes

• Expand Strava’s understanding of the relationship between user experiences and business performance and evolve org-wide metric strategies, connecting product development and marketing efforts to high quality results

• Lead deep root-cause investigations into business and product performance, developing novel approaches for problems that don't have an established playbook.

• Serve as a domain expert in inference for the data team horizontally, reviewing measurement designs and raising the bar for causal evidence quality across DS, Analytics, and cross-functional partners

What You’ll Bring to the Team:

• 5+ years of experience in data science or a related quantitative domain with experience owning measurement strategies and employing both experimental and quasi-experimental methods

• Depth in causal inference methods and their real-world failure modes, with the judgment to know when each approach is credible and when it isn't.

• Strong SQL proficiency and comfort writing Python for statistical data processing

• Python proficiency, with comfort writing production-quality code for statistical analysis and experiment tooling

• Ability to communicate quantitative findings as a clear narrative to technical and non-technical partners in product, finance, and senior leadership
  

For information on benefits, please click here.

Why Join Us?

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