# Lead Data Scientist

**Company:** [Bloomerang](https://hotfix.jobs/companies/bloomerang)
**Location:** Remote
**Role:** Data Science
**Salary:** $138k – $230k/yr
**Experience:** 8+ years
**Skills:** Python, SQL, scikit-learn, MLflow, Databricks, Causal Inference, A/B Testing, uplift modeling, gradient boosting, time series forecasting, llm evaluation
**Posted:** 2026-07-17

> Lead Data Scientist owning the intelligence layer over a nonprofit data foundation. Build causal experimentation, predictive models for donor behavior, model evaluation/monitoring, and ML lifecycle on Databricks/MLflow. 8+ years production ML experience with deep causal inference expertise required; principal-level IC using AI tools daily.

## Job Description

## 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).

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