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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 medallion lakehouse modeling.
Benefits
Health \+ Wellness
You'll have access to generous health, vision, and dental insurance options as well as HealthiestYou, a healthcare service that offers convenient, confidential access to quality doctors 24/7, anytime, anywhere.
Time Off
You'll get a competitive PTO package that includes 20 PTO days, 3 flex days, 4 optional volunteer days, 12 paid holidays, as well as paid parental leave. More is more!
401k
You'll receive a 401k match to help invest in your future.
Equipment
Everything you need to be successful, shipped right to your door. You got this. We got you.
Compensation
The salary range for this position is $138,100 \- $230,200\. You may also be eligible for a discretionary bonus. Actual compensation within the range will be dependent on your skills, experience, qualifications, and location, as well as applicable employment laws
Location
This is a permanent, full\-time, fully remote position (within the U.S. and select Canadian Provinces only). Employees living in Indianapolis, IN are welcome to work from our company headquarters. We do not offer Visa sponsorship or relocation assistance at this time.
Accommodations
Applicants who require accommodations may contact careers@bloomerang.com to request an accommodation in completing an application.
*Bloomerang is an Equal Opportunity Employer. Individuals seeking employment at Bloomerang are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation.*
Salary Context
This $138K-$230K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Bloomerang, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($184K) sits 5% below the category median. Disclosed range: $138K to $230K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Bloomerang AI Hiring
Bloomerang has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $230K - $230K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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