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# Lead Data Scientist, Data Products

- **Company:** Strava
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Files, Design of User Interfaces, Human-Computer Interaction, Python (Programming Language), Machine Learning, Strategies of Testing, Model Validation
- **Published:** October 26, 2026
- **Apply:** https://www.careerbuilder.com/job-details/lead-data-scientist-data-products-san-francisco-ca--88db25e1-f991-423c-8f2a-3314c4ec9166

## About the Role

Data Science, Metrics, Machine Learning, Problem Solving Skills, Leadership, Data Science, Data Modeling, Data Sets, Artificial Intelligence (AI), User Interface/Experience (UI/UX), * 5+ years of experience in data science or a related quantitative domain, including hands-on ownership of model evaluation and measurement for systems running in production.

 * Experience defining evaluation frameworks for ambiguous ML problems, including baseline selection, offline and online metric design, and validation strategies where ground truth is imperfect
 * Strong SQL proficiency and comfort writing production-quality Python for statistical data processing.
 * Fluency in metrics and measurement for consumer software products, and comfort working with cross-functional partners to translate business needs into technical plans and vice versa.

## Description

* Define what "good" looks like for Strava's internal models and the ML products built on top of it, setting the evaluation frameworks, offline and online metrics, and quality bars the team steers by.
 * Build the measurement layer for cross-domain ML products, scaling the org's visibility into performance across the business
 * Own experimentation and monitoring for production models and contribute to monitoring of key metrics and drift detection for enriched datasets, ensuring quality for downstream athlete-facing experiences.
 * Identify and size AI/ML product opportunities, translating ambiguous problem spaces into scoped initiatives with defined success criteria.
 * Serve as the data science domain expert for the team, raising the standard for baselines, validation, and evidence quality across ML engineers and cross-functional partners.