About the job
Join Our Mission to Empower Others: We’re Hiring!
At GoFundMe, we pride ourselves on being the world’s largest platform dedicated to social good, connecting individuals and nonprofits to foster a supportive community. Since our inception in 2010, our collective efforts have raised over $40 billion to help people in need. We are committed to making it easy and secure for anyone to seek assistance and support meaningful causes.
We are currently seeking a talented Staff Data Scientist specializing in Pricing to take the lead as a senior individual contributor. You will spearhead the scientific methods, strategic initiatives, experimentation, and AI implementation that drive pricing and yield optimization at GoFundMe. This pivotal role combines elements of economics, behavioral science, experimentation, and machine learning, directly influencing donation conversion rates, donation amounts, and overall donor experiences across our platform.
Please note, candidates must be located in the San Francisco Bay Area, as this position requires in-office attendance three times a week.
Your Role:
- End-to-End Ownership of Donation Pricing: Develop and implement analytical strategies, modeling frameworks, and key success metrics for pricing recommendations, balancing conversion rates, donation sizes, and long-term donor trust.
- Modeling Human Behavior: Utilize economic theories, behavioral science, and machine learning techniques to comprehend donor decision-making processes, assess elasticity, and forecast responses to alterations in product design.
- Leveraging Behavioral Signals: Analyze non-transactional behavioral signals (such as navigation patterns, hesitations, context, device usage, and timing) to identify shifts in intent and interaction patterns beyond direct transactions.
- Creating Adaptive Systems: Engineer models that evolve over time, integrating experimentation signals, feedback loops, and reinforcement learning concepts as needed.
- Leading Experimentation and Causal Learning: Collaborate with Product and Engineering teams to establish robust experimentation and measurement frameworks, ensuring that pricing and donation models maintain causal integrity and are safe for large-scale deployment.
- Incorporating External Data: Enhance behavioral models using external datasets (including macroeconomic indicators, seasonal trends, and regional signals) to gain deeper insights into donor behavior.
- Translating Insights into Action: Transform complex economic and behavioral analyses into actionable, deployable models.

