Lead Data Scientist
Bloomerang
$138,100–$230,200from the description
Jul 16, 2026
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Jul 21, 2026
What this job asks for AI summary
A principal-level individual contributor role focused on building and owning the data science layer of a nonprofit fundraising platform. The work centers on causal inference and experimentation, predictive modeling (donor retention, lifetime value, lapse risk), and ML lifecycle management on Databricks and MLflow. The role suits a seasoned applied data scientist with strong production ML experience who can set technical direction without managing a team.
Staff level · 8+ years · Remote · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 270 people nationally plausibly meet what this posting asks for (data scientists). range 160–410
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (9)
The title is 'Data Science Lead' with no explicit seniority level word, so the title states no level. However, the JD explicitly frames this as a 'principal-level individual contributor' seat with org-wide technical direction-setting across the Bloomerang Giving Platform — genuine Staff-level scope — so seniority is set to Staff.
The 8+ years requirement is stated at the role level ('8+ years building data science and machine learning') and is captured in total experience, not tied to any single named technology.
MLflow and Langfuse are listed together as acceptable alternatives for production model monitoring ('Langfuse, MLflow or similar'); MLflow is named first and is the preferred platform (Databricks-native), with Langfuse as the alternative.
Claude Code and Cursor appear in both the requirements section and the 'What You Will Do' section with firm language ('you already use… as a daily part of how you build'), making them hard gates on AI-native tooling; they are interchangeable options for the same requirement.
'Predictive modeling' and 'time-series forecasting' are named capability areas (propensity, churn/retention, LTV, forecasting) required by the JD; no single framework is mandated beyond scikit-learn and gradient boosting, so those are captured as the concrete tools.
LLM/agent evaluation frameworks and Data Vault 2.0 appear explicitly under 'Nice to Haves But Not Required' and are marked preferred accordingly.
The role is open to US residents and 'select Canadian Provinces'; the US-based flag is set to true per the caller's instruction and the primary US framing. No metro is inferred per caller instruction.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): predictive modeling.
Caller marked this a fully-remote role — scored against the national candidate pool.
Read the full posting
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