Staff Data Scientist

GoFundMe•San Francisco, CA

2d•$179,500 - $269,500•Hybrid

About The Position

GoFundMe is seeking a Staff Data Scientist to play a key role in shaping how the company utilizes data science to enhance its impact across marketing, growth, and product. This senior individual contributor will lead significant workstreams focused on improving business decisions related to audience strategy, acquisition, retention, personalization, experimentation, and investment efficiency. The role involves close collaboration with leaders in Data, Marketing, Product, Growth, Finance, and Engineering to establish technical strategy, develop robust measurement and modeling frameworks, leverage AI for workflow acceleration, and translate complex analyses into actionable recommendations for users, customers, and the business.

Requirements

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.

Nice To Haves

Experience with marketing measurement methods such as media mix modeling, multi-touch attribution, channel incrementality, forecasting, budget allocation, optimization, or ROI modeling.
Experience with web, mobile, product, or marketplace analytics tools such as Amplitude, Google Analytics, Optimizely, or GrowthBook.
Familiarity with modern data platforms and workflows such as Snowflake, Databricks, dbt, Airflow, Git, Looker, Tableau, or similar tools.
Experience operationalizing AI or ML workflows with engineering partners.

Responsibilities

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.