Staff Data Scientist, GoFundMe

• Either a Ph.D. in Economics, Applied Economics, or a closely related quantitative field, demonstrating the ability to push the boundaries of applied research and translate theory into practical modeling approaches OR 8+ 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
• Deep experience applying economic reasoning, causal inference, and behavioral modeling to real-world decision-making problems
• Demonstrated ability to own ambiguous, high-impact problems and deliver measurable business outcomes
• Strong foundation in econometrics, causal inference, and behavioral modeling
• Deep understanding of price elasticity, choice modeling, and decision science
• Experience modeling noisy, sparse, or non-transactional behavioral data
• Hands-on experience designing and interpreting experiments and causal signals
• Familiarity with reinforcement learning, bandits, or adaptive optimization concepts (applied or research-driven)
• Advanced proficiency in Python (pandas, NumPy, scikit-learn, PyMC/Stan or equivalent) and SQL, with the ability to build, validate, and iterate on complex analytical and modeling workflows
• Demonstrated ability to leverage modern AI tools and coding agents (e.g., LLM-based assistants, autonomous or semi-autonomous coding agents, model-driven feature generation, synthetic data generation) to accelerate research, prototyping, and productionization of models
• Experience designing or applying LLM-based or AI-assisted solutions to complex decisioning problems (e.g., feature extraction from unstructured data, rapid experimentation, simulation, or model orchestration), beyond basic prompt usage
• Exceptional ability to tell clear, compelling stories from complex data
• Comfortable influencing product direction and executive decision-making
• Demonstrated ability to lead without authority and elevate team practices

Desirable

• We’re looking for a Staff Data Scientist, Pricing to serve as the senior individual contributor driving the science, strategy, experimentation and AI deployment behind pricing and yield optimization at GoFundMe.
• This role sits at the intersection of economics, behavioral science, experimentation, and machine learning, with direct responsibility for optimizing donation conversion, donation amounts, and donor experience across the product
• Own donation pricing and amount optimization end-to-end: Define the analytical strategy, modeling frameworks, and success metrics for pricing recommendations across product surfaces, balancing conversion, donation amounts, and long-term donor trust
• Model human behavior using economics and AI: Apply economic theory, behavioral science, and machine learning to understand donor decision-making, estimate elasticity, and predict responses to changes in product design and choice architecture
• Leverage non-transactional behavioral signals: Model sparse and indirect signals (e.g., navigation, hesitation, context, device, timing) to detect shifts in intent and interaction patterns beyond observed transactions
• Build adaptive and reinforcement-aware systems: Design models that learn over time using experimentation signals, feedback loops, and reinforcement concepts (e.g., contextual bandits or sequential decision-making) where appropriate
• Lead experimentation and causal learning: Partner with Product and Engineering to design robust experimentation and measurement frameworks, ensuring pricing and donation models are causal-aware, interpretable, and safe to deploy at scale
• Incorporate external data and context: Augment behavioral models with external datasets (macroeconomic indicators, seasonality, regional or temporal signals) to better understand and anticipate donor behavior
• Translate insights into action: Convert complex economic and behavioral analyses into deployable models, clear product recommendations, and measurable business impact
• Influence through storytelling and leadership: Communicate insights effectively to senior leaders, humanize donor behavior through narrative, and serve as a trusted thought partner on pricing and donation strategy
• Raise the technical bar: Set best practices for modeling rigor, validation, monitoring, and iteration; mentor other data scientists and elevate pricing science across the organization