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
