Staff Data Scientist (Pricing)

GoFundMe•San Francisco, CA

10d•$179,500 - $269,500•Hybrid

About The Position

GoFundMe is seeking a Staff Data Scientist (Pricing) to be a senior individual contributor responsible for the science, strategy, experimentation, and AI deployment related to pricing and yield optimization. This role is at the intersection of economics, behavioral science, experimentation, and machine learning, with a focus on optimizing donation conversion, donation amounts, and the overall donor experience. The position requires candidates to be located in the San Francisco Bay Area and involves a 3-day per week in-office requirement.

Requirements

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.

Nice To Haves

Experience in consumer pricing, marketplaces, or digital payments/donations.
Experience partnering with engineering to productionize models and AI-driven systems, including monitoring, evaluation, and iteration in live environments.
Familiarity with modern data platforms (Snowflake, Databricks) and experimentation infrastructure; experience with model versioning and validation is a plus.
Strong data visualization, documentation, and presentation skills, with an emphasis on clarity and executive-ready communication.

Responsibilities

Own donation pricing and amount optimization end-to-end, including defining 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 by applying 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, such as navigation, hesitation, context, device, and timing, to detect shifts in intent and interaction patterns beyond observed transactions.
Build adaptive and reinforcement-aware systems by designing models that learn over time using experimentation signals, feedback loops, and reinforcement concepts like contextual bandits or sequential decision-making.
Lead experimentation and causal learning by partnering with Product and Engineering to design robust experimentation and measurement frameworks, ensuring pricing and donation models are causal-aware, interpretable, and safe for large-scale deployment.
Incorporate external data and context, such as macroeconomic indicators, seasonality, and regional or temporal signals, to augment behavioral models and better understand and anticipate donor behavior.
Translate complex economic and behavioral analyses into deployable models, clear product recommendations, and measurable business impact.
Influence through storytelling and leadership by communicating insights effectively to senior leaders, humanizing donor behavior through narrative, and serving as a trusted thought partner on pricing and donation strategy.
Raise the technical bar by setting best practices for modeling rigor, validation, monitoring, and iteration; mentoring other data scientists and elevating pricing science across the organization.