GoFundMe is looking for a Staff Data Scientist to drive the evolution of performance marketing science, owning the technical roadmap, modeling frameworks, and AI capabilities to optimize marketing investment decisions and improve campaign efficiency.
Requirements
- Advanced proficiency in Python (NumPy, pandas, scikit-learn) and SQL (window functions, performance tuning).
- Hands-on experience designing, analyzing, and interpreting A/B tests and experimentation frameworks.
- Expertise in statistical inference, causal modeling (regression, propensity scoring), uplift modeling, and marketing attribution models (multi-touch attribution, Markov chains).
- Proven track record in time-series forecasting and budget allocation models.
- Familiarity with ETL frameworks, data warehousing, and APIs; hands-on experience with Databricks and Snowflake is a plus.
- Data visualization skills using Looker or equivalent BI tools; strong storytelling and presentation abilities.
- Version control proficiency (Git) and collaborative coding workflows.
Responsibilities
- Lead end-to-end marketing data science initiatives: Define analytical strategy, develop models, and deliver insights that drive measurable impact on user acquisition, retention and efficiency.
- Develop advanced attribution and causal frameworks: Build and/or evolve models including multi-touch attribution, media mix modeling, causal inference, and uplift modeling to quantify incremental impact.
- Advance predictive and prescriptive modeling: Design time-series, optimization, and budget allocation models to forecast ROI and guide marketing investment decisions.
- Operationalize AI in marketing workflows: Research and implement AI/ML systems for targeting, bidding, and creative optimization by leveraging predictive and generative methods to improve campaign efficiency.
- Enhance experimentation frameworks: Partner with engineering to expand statistical testing infrastructure, ensuring scalable, accurate causal measurement across campaigns and channels.
- Act as a technical thought leader: Shape best practices in feature engineering, model versioning, validation, and deployment for marketing science applications.
- Explore adaptive experimentation: Research, apply and refine multi-arm and contextual bandit algorithms to optimize budget allocation and message selection in near real-time.
Other
- Candidates considered for this role will be located in the San Francisco, Bay Area.
- There will be an in-office requirement of 3x a week.
- Collaborate cross-functionally: Partner with Marketing, Growth, and Finance teams to interpret insights, influence strategy, and align data science work with business goals.
- Mentor and elevate: Guide other data scientists technically through design reviews, model audits, and shared learning sessions, helping scale best practices across the org.
- Exceptional communication skills: able to influence non-technical stakeholders and present complex analyses to executive audiences.
