Lead Data Scientist

What You Will Do

• Design and run the experimentation engine—randomized holdouts, uplift measurement, significance and power—to prove causation in fundraising actions impacting retention or giving.
• Build predictive and forecasting models for donor lifetime value, retention, lapse risk, and goal forecasting—calibrated, explainable, and honest about uncertainty.
• Own model quality, evaluation, accuracy bars, and monitoring to keep AI products trustworthy.
• Manage the ML lifecycle on Databricks and MLflow—training, deployment, versioning, drift and performance monitoring.
• Set technical direction for data science: define modeling, measurement, validation methods and tooling; raise rigor through own work.
• Partner daily with data engineers on the lakehouse and AI engineers on product shipping.
• Use AI tools (Claude Code, Cursor, or similar) daily for analysis, modeling, evaluation, and problem-solving.

What You Need to Succeed

Technical Depth

• 8+ years building data science and machine learning that shipped to production and moved real metrics.
• Deep, hands-on experience with causal inference, A/B testing, randomized holdouts, uplift and treatment-effect modeling, significance and power analysis.
• Expertise in predictive and statistical modeling: propensity, churn, retention, lifetime value, time-series, forecasting, and calibration.
• Strong Python and SQL; fluency with scikit-learn, gradient boosting, and experiment design tooling.
• Experience deploying, versioning, and monitoring models (Langfuse, MLflow or similar), owning post-ship outcomes including drift.
• Comfortable on a modern data lakehouse (Databricks preferred) and partnering on feature data models.

AI-Native Mindset

• Daily hands-on use of AI tools like Claude Code or Cursor; able to articulate acceleration and limitations.
• Curiosity about AI frontier, including LLM and agent evaluation.

Leadership & Ownership

• Technical leadership through work clarity, rigor, standards, and influence (principal-level IC, not people management).
• Quality-first approach: build in evaluation, monitoring, and honest uncertainty.
• Track record partnering with data engineers, ML/AI engineers, and product.
• Serious about data security, trust, and consent for donor data analytics.

Nice to Haves

• Background in nonprofit, fundraising, or CRM data.
• Causal/experimentation work at product scale (experimentation platforms, sequential testing).
• LLM and agent evaluation frameworks.
• Familiarity with Data Vault 2.0 or medallion lakehouse modeling.

Benefits

• Generous health, vision, dental insurance; HealthiestYou 24/7 service.
• Competitive PTO: 20 days + 3 flex + 4 volunteer + 12 holidays + parental leave.
• 401k match.
• Equipment provided.
• Salary range $138,100 - $230,200 + discretionary bonus (dependent on skills, experience, qualifications, location).