Senior Manager, Data Science

GoFundMe•San Francisco, CA

9d•$243,000 - $364,000•Hybrid

About The Position

GoFundMe is seeking a Senior Manager, Data Science & Analytics to lead and grow the teams responsible for data-driven decision-making. This role involves setting the strategic direction for data science and analytics across the company, enhancing data maturity and analytical rigor, and transforming complex questions into actionable insights. The manager will lead experienced data scientists and technical leads, fostering strong partnerships with various departments including Product, Engineering, Marketing, Finance, and Trust & Safety. The position requires a blend of strategic leadership and hands-on engagement in high-impact projects, with a focus on building organizational capabilities, practices, and talent for sustained value delivery. Candidates must be located in the San Francisco Bay Area and adhere to a 3-day per week in-office requirement.

Requirements

8+ years in data science, analytics, or a related quantitative field, including significant experience leading teams or cross-functional initiatives.
Experience setting direction and delivering impact across multiple business or product domains.
Track record of developing data science and analytics talent, including senior individual contributors and emerging leaders.
Proficiency in Python and SQL; able to assess analytical approaches and guide technically complex work.
Deep experience with experimentation, causal inference, and measurement, and translating findings into business or product decisions.
Experience with forecasting, optimization, predictive modeling through machine learning or other methods that support organizational decisions.
Ability to assess data models, metrics, workflows, and platforms, and partner effectively with Engineering and Data Platform.
Experience leading through senior individual contributors, technical leads, or managers and developing people as the organization grows.
Strong communication and executive influence; able to explain analytical findings clearly and practically.
Ability to set vision and priorities, balance long-term capability building with near-term results, and apply sound judgment about where analysis, experimentation or AI can improve decisions.

Nice To Haves

Master’s or Ph.D. in statistics, mathematics, economics, computer science, physics, operations research, or a related field or equivalent applied experience. Applied experience carries more weight than education.
Experience with BI and analytics tools such as Looker, Hex, or equivalent is a plus.
Marketing science expertise in media mix modeling, multi-touch attribution, uplift modeling and/or marketing experimentation is a strong plus.
Data science experience in consumer products, marketplaces, nonprofits, payments, or fundraising.
Marketing and growth measurement experience, including MMM, attribution, audience segmentation, or campaign optimization.
Familiarity with product experimentation and web or mobile analytics tools such as Optimizely, GrowthBook, Google Analytics, or Amplitude.
Experience applying generative AI or LLMs to products or workflows.
Familiarity with machine learning operations, model monitoring, or scalable data transformation.

Responsibilities

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.
Develop the organization: Lead and develop experienced data scientists and technical leads. Build leadership capacity, strengthen collaboration across teams, and create clear expectations for quality and impact.
Advance applied AI and modeling: Identify opportunities to use predictive, causal, and generative AI methods to improve products, operations, and business decisions, partnering with the teams responsible for production systems where appropriate.
Communicate with clarity: Translate complex analysis into concise actionable recommendations, communicate tradeoffs, and help senior leaders make informed decisions.