Lead Data Scientist

About this role

At Bloomerang, we believe change happens on purpose. We champion the power and potential of nonprofits, igniting next-level impact with the team and technology built for purpose. Our powerful giving platform and stellar support enable tens of thousands of nonprofits to raise more, recruit more, and retain more, fueling maximum impact and raising the bar on what’s possible for the nonprofit sector. That's why, even as the nonprofit sector sees declines in giving, Bloomerang customers raise more year over year.

We're also in the business of creating thriving employees. Join a mission-driven culture built on our core values of Simplify, Care and Act. We know our people are the key to our success, and we're proud to be home to some of the most innovative and skilled individuals in the workforce today. Come feel invigorated and unstoppable with us!

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.

• Causal inference and experimentation: deep, hands-on work with A/B testing, randomized holdouts, uplift and treatment-effect modeling, and significance and power analysis. You know why measuring impact against KPIs without a control group is the most common way to learn the wrong lesson.

• Predictive and statistical modeling: propensity, churn and retention, lifetime value, time-series and forecasting, and calibration—with the judgment to reach for the simplest model that works.

• Strong Python and SQL, and fluency with the modern ML and statistics stack (e.g., scikit-learn, gradient boosting, and the tooling behind experiment design).

• Production ML sensibility: real experience deploying, versioning, and monitoring models (Langfuse, MLflow or similar). You own outcomes after the model ships, including drift and degradation.

• Modern data platform fluency: comfortable working on a lakehouse (Databricks preferred) and partnering closely on the data models your features depend on.

AI-Native Mindset

• Hands-on AI tool usage: you already use Claude Code, Cursor, or similar AI development environments as a daily part of how you build. You can speak to where they accelerate your work and where they don't.

• Curiosity about the frontier: you're energized by the pace of AI-driven change—including LLM and agent evaluation—and you bring that energy into the team.

Leadership & Ownership

• Technical leadership without the org chart: you set direction through the clarity and rigor of your work, your standards, and your influence. This is a principal-level individual-contributor seat, not a people-management one.

• Quality-first instincts: you build evaluation, monitoring, and honest uncertainty in from day one. You'd rather ship a calibrated “we're not sure yet” than a confident answer that's wrong.

• Cross-functional partnership: a track record of working well with data engineers, ML and AI engineers, and product.

• Security and data trust: our customers trust us with their donors' data. You take that—and the consent posture behind any cross-organization analytics—seriously.

Nice to Haves But Not Required

• Background in nonprofit, fundraising, or CRM data.

• Causal and experimentation work at product scale (experimentation platforms, sequential testing).

• LLM and agent evaluation frameworks and techniques.

• Familiarity with Data Vault 2.0 or m

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