Lead Data Scientist

The Role

As a Data Science Lead at Bloomerang, you'll own the intelligence layer on top of the Unified Data Foundation (UDF)—the models, experiments, and measurement that turn the data of 24,000+ nonprofits into products they can trust. Reporting to the Director of AI Product Engineering, you'll set the technical direction for data science across the Bloomerang Giving Platform: the causal measurement that proves what actually works, the predictive and forecasting models that anticipate donor behavior, the evaluation that keeps our AI products trustworthy, and the ML platform that gets all of it to production.

This is a hands-on, builder role—a principal-level individual contributor who leads through the work, not a people-management seat. Data is the moat; intelligence is the castle. You'll prove causation instead of settling for correlation, set the bar for how models are built, measured, and shipped, and partner daily with data engineers, AI engineers, and product. You'll bring AI-native habits into how you build, test, and reason.

What You Will Do

• Prove what works, not just what correlates. Design and run the experimentation engine—randomized holdouts, uplift measurement, significance and power—so we can claim a fundraising action caused a lift in retention or giving, not that it happened alongside one.
• Build the predictive and forecasting models that drive donor lifetime value, retention, lapse risk, and “will we hit our goal?” forecasting—calibrated, explainable, and honest about uncertainty rather than falsely precise.
• Own model quality and evaluation. Stand up the evals, accuracy bars, and monitoring that keep our AI products and agents trustworthy—because a confident wrong answer costs a fundraiser more than no answer at all.
• Get models to production and keep them healthy. Own the ML lifecycle on Databricks and MLflow—training, deployment, versioning, and drift and performance monitoring—so models keep earning trust long after launch.
• Set the technical direction for data science. Define how we model, measure, and validate; make the call on methods and tooling; and raise the rigor bar through the quality of your own work.
• Partner across the stack. Work daily with the data engineers building the data lakehouse, the AI engineers shipping the products.
• Use AI tools (Claude Code, Cursor, or similar) daily for analysis, modeling, evaluation, and problem-solving. We expect this to fundamentally change how you work, not just speed up what you'd do anyway.

What You Need to Succeed

Technical Depth

• Applied data science experience: 8+ years building data science and machine learning that shipped to production and moved a real metric—not models that stalled in a notebook.