Staff Data Scientist - Sales Intelligence

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GoFundMe is the world’s most powerful community for good, dedicated to helping people help each other. By uniting individuals and nonprofits in one place, GoFundMe makes it easy and safe for people to ask for help and support causes—for themselves and each other. Together, our community has raised more than $40 billion since 2010. Join us!

We’re looking for a Staff Data Scientist to drive the evolution of Sales Intelligence at GoFundMe. This senior individual contributor will own the technical roadmap, modeling frameworks, and AI capabilities that help our Sales and Customer Success teams acquire, retain, and grow our non-profit (NPO) partners. You’ll work at the intersection of data science, AI, and go-to-market strategy, using advanced machine learning and natural language models to unlock insights from a wide range of data - both from internal and external data sources. Your work will shape how our teams prioritize leads, understand customer health, and personalize outreach at scale.

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.

The Job

• Lead the development of sales intelligence models: Design, prototype, and deploy models that score leads, predict churn, and surface upsell and cross-sell opportunities across our NPO customer base.
• Develop and evolve customer health frameworks: Build predictive models that quantify customer engagement, satisfaction, and retention risk, helping Sales and Success teams proactively address churn.
• Operationalize AI for sales enablement: Leverage NLP, embeddings, and large language models (LLMs) to extract, normalize, and retrieve insights from structured and unstructured sources - including sales notes, emails, call transcripts, CRM data, and support tickets - creating a unified knowledge layer for intelligent sales workflows.
• Build data-driven playbooks: Create systems that recommend relevant talking points, products, or case studies for sales meetings based on historical interactions and customer attributes.
• Advance predictive and prescriptive modeling: Develop time-series and optimization models to forecast sales outcomes and recommend next-best actions for account growth.
• Partner with GTM systems and data engineering: Ensure robust data pipelines, feature stores, and feedback loops that enable continuous model improvement.
• Act as a technical thought leader: Define best practices for model validation, feature engineering, and productionization in go-to-market (GTM) AI applications.
• Collaborate cross-functionally: Partner with Sales, Marketing, Customer Success, and Product teams to interpret insights, influence strategy, and embed intelligence directly into sales workflows.
• Mentor and elevate: Provide technical mentorship to data scientists and analysts, helping scale advanced analytical and AI practices across the Decision Science team.

You

Experience & Education

• 8+ years of experience in data science or applied ML, with at least 4–5 years focused on sales analytics, revenue intelligence, or customer lifecycle modeling.
• Master’s or Ph.D. in a quantitative discipline (Statistics, Economics, Computer Science, Mathematics, or related field) or equivalent applied experience.

Technical Skills

• 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.

Leadership & Collaboration

• Exceptional communication skills: able to influence non-technical stakeholders and present complex analyses to executive audiences.
• Demonstrated ability to define technical strategy, elevate team practices, and drive high-impact initiatives.
• Experience mentoring and developing junior data professionals.

Preferred

• Expe