Staff Data Scientist
Job description
About the role
Staff Data Scientist (Pricing) at GoFundMe
The Position
GoFundMe is the world's largest platform for people to support one another through fundraising. The company connects individuals and nonprofits, enabling users to ask for help and champion causes. Since its creation, the community has raised over $40 billion. The Staff Data Scientist (Pricing) role is a senior individual contributor position focused on the science, strategy, and execution behind donation optimization. This position sits at the crossroads of economics, behavioral science, and experimentation, applying advanced techniques to improve conversion, donation amounts, and the long-term health of the donor relationship. The role reports into the Pricing discipline and requires collaboration with Product, Engineering, and Analytics teams.
Candidates for this position must be located in the San Francisco Bay Area and able to work in the office three days per week.
Responsibilities
The Staff Data Scientist (Pricing) is responsible for end-to-end ownership of donation pricing and amount strategies. This involves defining analytical approaches, selecting modeling frameworks, and establishing success metrics to guide pricing recommendations across all GoFundMe surfaces. The role requires balancing short-term conversion goals with the long-term objective of building donor trust.
A core part of the position involves using advanced techniques to understand donor behavior. This includes applying economic theory, behavioral science, and machine learning to analyze how donors make decisions. The role estimates price elasticity and predicts reactions to changes in product design and user experience.
The position also focuses on leveraging non-transactional signals to understand user intent. This involves modeling sparse and indirect data such as navigation patterns, hesitation, context, device type, and timing to uncover behavioral insights that are not visible in transactional data alone.
The role designs adaptive systems that incorporate experimentation and feedback loops. This includes using reinforcement concepts and sequential decision-making approaches, such as contextual bandits, to enable models that learn and improve over time.
Staff Data Scientists partner with Product and Engineering teams to build robust experimentation and measurement frameworks. These frameworks ensure that pricing and donation models are causal-aware, interpretable, and safe for large-scale deployment. The role also involves incorporating external data, including macroeconomic indicators, seasonality, and regional or temporal signals, to better understand and anticipate donor behavior.
Finally, the position requires translating complex analytical findings into actionable models and recommendations. The Staff Data Scientist must communicate insights effectively to senior leadership, using storytelling to humanize donor behavior and serve as a strategic thought partner on pricing and donation strategy. The role sets best practices for modeling rigor, validation, monitoring, and iteration, while mentoring other data scientists and elevating the discipline of pricing science within the organization.
Qualifications
To be considered for this role, candidates must hold either a Ph.D. in Economics or a closely related quantitative field. The academic background should demonstrate the ability to conduct applied research and translate theoretical concepts into practical modeling approaches. Alternatively, candidates may present eight or more years of industry experience in data science, applied economics, pricing, marketplace optimization, or monetization at a high-tech digital company, with a proven track record of owning and scaling pricing or decisioning systems.
Candidates must have a deep history of applying economic reasoning, causal inference, and behavioral modeling to real-world decision-making problems. They must also demonstrate the ability to own ambiguous, high-impact problems and deliver measurable business outcomes.
Core Competencies
- Advanced knowledge of econometrics, causal inference, and behavioral modeling.
- Deep understanding of price elasticity, choice modeling, and decision science.
- Experience modeling sparse, indirect, or non-transactional behavioral data.
- Hands-on experience designing and interpreting experiments and causal signals.
- Familiarity with reinforcement learning or contextual bandits is a plus.
Tools and Techniques
- Econometrics, causal inference, behavioral modeling, experimentation, price elasticity, choice modeling, machine learning.
Practical Information
-
Location: USA
-
Engagement: In-office requirement of three days per week.
-
Compensation: The base salary for this role is $180,000, with an expected total compensation of $240,000 over a four-year period. These figures are estimates and may vary based on individual qualifications and market conditions.
What you'll do
- Meet the bar Practical notes