Data Science and Analytics Manager

Want to help us help others? We’re hiring! 

GoFundMe is the world’s most powerful community for good, dedicated to helping people help each other. By uniting individuals and nonprofits in one place, GoFundMe makes it easy and safe for people to ask for help and support causes, for themselves and each other. Together, our community has raised more than $40 billion since 2010.

We’re looking for a Senior Manager, Data Science & Analytics to lead and grow the teams that help GoFundMe make better decisions. You’ll set the direction for data science and analytics across the company, strengthen data maturity and analytical rigor, and help teams turn complex questions into clear, actionable insights.

You’ll lead through experienced data scientists and technical leads, building strong partnerships across Product, Engineering, Marketing, Finance, Trust & Safety etc. You’ll balance strategic leadership with close engagement in high-impact work, and help the organization build the capabilities, practices, and talent needed to deliver lasting value.

Candidates considered for this role will be located in the San Francisco Bay Area. There will be an in-office requirement of 3x a week.

The Job

• Set the direction for Data Science & Analytics: Define a company-wide vision, priorities, and operating practices that connect analytical work to GoFundMe’s most important goals.
• Raise data maturity and rigor across the company: Improve how teams frame questions, measure outcomes, use experiments, and apply evidence to decisions.
• Lead a broad portfolio of work: Guide data science and analytics across all the functions that make GoFundMe successful.
• Focus on measurable impact: Prioritize work that improves decisions and outcomes, and establish clear ways to assess its value over time.
• Strengthen analytical foundations: Set standards for trusted metrics, high-quality analysis, reproducible work, documentation, and responsible use of data and AI. Partner with Engineering and Data Platform on the foundations and infrastructure that support these practices.
• Guide end-to-end delivery: Ensure teams take important work from problem definition and analysis through recommendations, implementation, and measurement of results.
• Build strong cross-functional partnerships: Work with leaders across the company to understand their needs, align priorities, and make data science and analytics a trusted partner in decision-making.
• Lead and develop experienced data scientists and technical leads. Build leadership capacity, strengthen collaboration across teams, and create clear expectations for quality and impact.