Lead Data Scientist, Inference — Causal Metrics (Hybrid)

Strava, Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 195,000; Full time

Job summary

Strava, Inc. is seeking a senior Data Scientist to join the Data Science team in a strategic contributor role. You will link product and marketing efforts to measurable outcomes, developing causal frameworks and models to guide metric strategy across the company.

You will design and lead measurement initiatives, applying advanced causal methods, and collaborate with cross-functional partners to drive high-leverage results for Strava’s athletes and business.

Qualifications

• 5+ years of experience in data science or a related quantitative domain.
• Depth in causal inference methods and their real-world failure modes.
• 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

Responsibilities

• Design measurement strategies for complex initiatives using experimental and quasi-experimental methods to quantify business outcomes.
• Expand understanding of the relationship between user experiences and business performance and evolve metric strategies.
• Lead deep root-cause investigations into business and product performance with novel approaches.
• Serve as a domain expert in inference, reviewing measurement designs and raising the bar for causal evidence quality across teams.

Skills

Data science

Causal inference

SQL

Python

Communication

Job description

Strava, Inc. is seeking a senior Data Scientist to join the Data Science team in a strategic contributor role. You will link product and marketing efforts to measurable outcomes, developing causal frameworks and models to guide metric strategy across the company.

You will design and lead measurement initiatives, applying advanced causal methods, and collaborate with cross-functional partners to drive high-leverage results for Strava’s athletes and business.