Lead Data Scientist (Inference)

• 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

Benefits

• 100% company paid benefits for employees and families
• Flexible paid time off
• $2,000 annual professional development stipend
• Paid time off for volunteering
• 401(k) plan - with company matching
• $1000 annual gear stipend
• $500 annual gym or coaching reimbursement
• On-site fitness rooms with showers, lockers, and towel service
• Weekly team workouts – including remote employees
• Safe and secure bike storage on-site
• Weekly all hands meetings
• Employee resource groups
• Anti-racism nonprofit company matching program
• Free lunch on Wednesdays on-site
• Regular team happy hours
• Team offsites and company retreats
• Cell phone reimbursement
• Generous industry discounts on gear and activities
• Race entries to events we sponsor
• Snacks and stocked kitchens on-site
• Strava gear
• Stock options- Strong SQL proficiency and comfort writing Python for statistical data processing
• 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
• Ability to communicate quantitative findings as a clear narrative to technical and non-technical partners in product, finance, and senior leadership
• 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
• Python proficiency, with comfort writing production-quality code for statistical analysis and experiment tooling