Manager, Data Science (Marketing)

GoFundMe•San Francisco, CA

2d•$202,000 - $303,000•Hybrid

About The Position

GoFundMe is seeking a Data Science Manager to lead the next generation of marketing data science. This role will build and scale applied data science and AI foundations to empower Marketing, Growth, and Finance teams in making data-driven investment decisions. The manager will act as a player-coach for a team of data scientists, driving innovation and ensuring delivery excellence. The position requires candidates to be located in the San Francisco Bay Area, with an in-office requirement of 3 days per week.

Requirements

8+ years of experience in data science roles with direct impact on marketing, growth, or revenue optimization.
Master’s or Ph.D. in a quantitative field (Statistics, Mathematics, Economics, Computer Science, Physics, Operations Research or related), or equivalent applied experience.
Advanced proficiency in Python (NumPy, pandas, scikit-learn) and SQL (window functions, optimization).
Deep experience with experimentation frameworks: A/B testing, causal inference, uplift modeling, and attribution models.
Proven success in forecasting, optimization, and budget allocation models for marketing and growth functions.
Hands-on with data platforms (Snowflake, Databricks) and BI tools (Looker, Tableau, or equivalent).
Strong data storytelling and executive presentation abilities.
Exceptional communication skills with the ability to influence executive stakeholders and translate data into actionable business recommendations.
Experience developing senior data scientists and elevating team practices.
Demonstrated ability to define a strategic vision for applied data science in marketing, balancing rapid experimentation with long-term infrastructure investments.

Nice To Haves

Familiarity with experimentation and web/mobile analytics platforms (Optimizely, GrowthBook, Google Analytics, Amplitude).
Experience integrating with marketing APIs (Google, Meta, programmatic platforms) for campaign optimization.
Prior exposure to generative AI or LLMs in marketing use cases (e.g., personalization, targeting, creative analysis).
Knowledge of multi-arm and contextual bandit algorithms for adaptive experimentation and continuous marketing optimization.
Familiarity with ML ops practices: version control, model monitoring, scalable ETL frameworks.

Responsibilities

Build a strong AI and data science foundation: Develop scalable pipelines, reusable modeling frameworks, and robust experimentation platforms to support marketing and growth decision-making.
Lead end-to-end data science & AI projects: From requirements gathering through feature engineering, modeling, validation, deployment, and monitoring.
Establish best practices: Champion standards in model governance, reproducibility, data quality, and system reliability to ensure sustainable and trustworthy AI adoption.
Drive marketing science innovation: Apply advanced methods—causal inference, uplift modeling, multi-touch attribution, and media mix modeling—to unlock insights and optimize spend.
Advance forecasting & ROI modeling: Deliver budget allocation frameworks and predictive models that guide long-term roadmap planning and marketing efficiency.
Partner cross-functionally: Work closely with Marketing, Growth, Product, Engineering, and Finance leaders to align analytics initiatives with revenue impact.
Invest in people: Mentor, coach, and elevate a team of high-performing data scientists; foster a culture of technical rigor, curiosity, and applied innovation.
Push the frontier of applied AI in marketing: Evaluate emerging generative and predictive AI approaches for audience segmentation, creative optimization, personalization, and campaign efficiency.