Staff Data Scientist, GoFundMe

• 8+ years of experience leading data science, applied statistics, or machine learning projects with measurable business impact
• Master’s degree or Ph.D. in a quantitative field, or equivalent applied experience coupled with a bachelor’s degree
• Experience with marketing, growth, marketplace, and/or product optimization problems
• Strong command of statistical inference, causal inference, uplift modeling, heterogeneous treatment effects, treatment/control frameworks, and incrementality measurement
• Experience applying machine learning to targeting, personalization, segmentation, recommendation, lifecycle optimization, or similar problems
• Familiarity with adaptive experimentation, including frequentist A/B tests, multi-arm bandits, contextual bandits, and/or related optimization methods
• Advanced proficiency with SQL and Python for data extraction, transformation, modeling, and analysis
• Experience using AI-assisted tools to increase the speed and quality of analysis, coding, iteration, and communication
• Ability to define technical strategy, identify reusable patterns, and raise the quality of data science work across teams
• Strong business judgment and stakeholder management skills
• Excellent communication and storytelling skills, including experience presenting to executive audiences
• Experience mentoring data scientists, analysts, or other technical team members

Desirable

• We’re looking for a Staff Data Scientist to help shape how GoFundMe uses data science to grow our impact across marketing, growth, and product
• This role will serve as a senior individual contributor who leads high-impact workstreams across go-to-market and product surfaces, with a primary focus on helping the business make better decisions about audience strategy, acquisition, retention, personalization, experimentation, and investment efficiency
• You will partner closely with leaders across Data, Marketing, Product, Growth, Finance, and Engineering to define technical strategy, build scalable measurement and modeling frameworks, accelerate impact through modern AI-enabled workflows, and translate complex analyses into clear recommendations that improve outcomes for our users, customers, and business
• Lead high-impact data science initiatives across marketing, growth, and product, from problem definition through methodology, execution, interpretation, and recommendation
• Define org-level technical strategy, identify high-leverage opportunities, and establish best practices for experimentation, causal measurement, modeling, AI-assisted development, and decision science
• Design, analyze, and interpret experiments and quasi-experiments; build scalable frameworks to estimate incrementality, treatment effects, and long-term business value
• Develop ML, uplift, heterogeneous treatment effect, and adaptive experimentation models to improve targeting, segmentation, personalization, lifecycle optimization, audience strategy, budget allocation, and product decisions
• Partner with Marketing, Growth, Sales, Finance, and Product to evaluate acquisition, retention, ROI, user journeys, funnels, marketplace dynamics, donation conversion, and engagement
• Use AI tools to accelerate prototyping, analysis, coding, debugging, documentation, and repetitive workflows without compromising quality, accuracy, or review standards
• Translate complex analyses into clear recommendations for technical, non-technical, and executive audiences
• Collaborate with Data, Analytics, and Product Engineering to improve instrumentation, data quality, experimentation infrastructure, and reusable data science workflows
• Mentor data scientists and analysts through design reviews, model reviews, methodology discussions, AI-enabled workflows, and shared best practices