Senior Inference Scientist — Causal Analytics for Growth

Strava; San Francisco (CA); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Strava is seeking a senior Data Scientist to lead measurement and causal inference initiatives that connect product and marketing efforts to athlete outcomes.

In a flexible hybrid model, you’ll spend over half your time on-site in San Francisco, collaborating with product, analytics, and leadership to raise the bar for data-driven decisions.

You’ll design advanced experiments, tackle complex problems with novel approaches, and mentor teams on rigorous evidence and scalable analysis.

Qualifications

• 5+ years of experience in data science with measurement strategy ownership.
• Depth in causal inference methods and understanding of real-world failure modes.
• Strong SQL proficiency and comfort writing Python for statistical data processing.
• Python proficiency for production-quality code in statistical analysis and experiment tooling.
• Ability to communicate quantitative findings clearly to technical and non-technical partners.

Responsibilities

• Design measurement strategies for Strava's complex initiatives using experimental and quasi-experimental methods to quantify outcomes.
• Expand understanding of user experiences and business performance, aligning metric strategy across teams.
• Lead deep root-cause investigations and develop novel approaches for problems without established playbooks.
• Serve as a domain expert in inference, elevating causal evidence quality across DS, Analytics, and partners.

Skills

Data science

Causal inference

SQL

Python

Communication

Job description

Strava is seeking a senior Data Scientist to lead measurement and causal inference initiatives that connect product and marketing efforts to athlete outcomes.

In a flexible hybrid model, you’ll spend over half your time on-site in San Francisco, collaborating with product, analytics, and leadership to raise the bar for data-driven decisions.

You’ll design advanced experiments, tackle complex problems with novel approaches, and mentor teams on rigorous evidence and scalable analysis.