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About This Role
Company Description
Givebutter is the most-loved nonprofit fundraising and CRM platform, empowering millions of changemakers to raise more, pay less, and give better. Nonprofits use Givebutter to replace multiple tools so they can launch fundraisers and events, use donation forms and donor management (CRM), send emails and text blasts—all in one place. Use of the Givebutter platform is completely free with a 100% transparent tip-or-fee model.
Givebutter has been certified as a Great Place to Work every year since 2021, and is the #1 rated nonprofit software company on G2 across multiple categories.
Our mission is to empower the changemaker in all of us. We believe giving should be fun, so you’ll want to do it again, and we also believe that work should be fun, so that you’ll have the greatest impact. We are excited to hear from talented people who want to work with other talented people in making the world a butter place—and have fun along the way.
Role Description
Givebutter is hiring a talented and passionate Product Data Scientist to join our growing Data team. We’re looking for someone to be a strong partner and collaborator, working with engineers, analysts, and Product stakeholders to deeply understand the most impactful business needs and build solutions that scale as we grow.
We want to hear from people who…
- Have experience turning product event data: clickstream, funnel events, user sessions, feature interactions, into behavioral insights that help product teams understand what users are actually doing, not just what they intend to do.
- Have collaborated with Product and Revenue teams, understanding how each function uses data differently, and what it takes to build analyses that influence decisions rather than just document what happened.
- Have designed, run, and communicated the results of A/B experiments in a practical context, including the harder work of navigating inconclusive results and helping stakeholders make confident decisions without clean answers.
- Have demonstrated experience defining and governing product metrics: activation rates, retention curves, engagement signals, conversion funnels, and building the dashboards and reports that make those metrics something stakeholders trust and return to.
- Have hands-on experience using AI tools to accelerate and enhance analytical work, and a genuine curiosity about what it looks like when agents do the analysis, not just assist it.
Responsibilities
Product Data Modeling
- Gain a deep understanding of the product team's business needs and data sources, partnering with stakeholders to understand what's needed in the short- and medium-term.
- Working with our Analytics Engineering team, own the definition, logic, and documentation of the data model that supports product analytics needs, with a focus on quality, usability, and clear documentation in a fast-moving product environment.
Product & Revenue Analytics
- Manage the inflow of data requests from product and revenue stakeholders, ensuring clear two-way communication on needs, expectations, and progress, and high-quality, timely delivery.
- Drive the strategic product analytics roadmap as the business grows, identifying the analyses and capabilities that will matter most before they're explicitly requested.
User Behavior & Experimentation
- Be a subject matter expert on how users move through the product, driving and developing the company's understanding of activation, engagement, and retention through behavioral analyses, funnel work, and product event data.
- Design, run, and communicate the results of A/B experiments end to end, applying statistical rigor in a practical context and helping stakeholders make confident decisions, including when results are ambiguous.
Product Metrics Management
- Own the definition, logic, and documentation of the product metrics that the business runs, ensuring consistency in how key measures are defined, calculated, and communicated across teams.
- Partner with product stakeholders to evolve the metrics framework as the product grows, identifying where existing metrics are insufficient or misleading and driving alignment on what should replace them.
Agentic Analytics Support
- Contribute domain expertise and evaluation rigor to our in-house agentic analytics framework, helping define what good outputs look like across our query, analytics, insights, and reporting agents.
- Collaborate with Analytics Engineering on building eval suites for AI-generated analytical outputs, participate in agent improvement cycles, and help translate business intuition into the semantic and prompt layer that makes agents reliably useful.
Requirements
- 4+ years of experience in Product Analytics, experimentation, and working with product event or clickstream data.
- Advanced degree in Statistics, Computer Science, Mathematics or a related field, or relevant industry experience.
- Expert querying and data manipulation skills using SQL and Python (or any coding language).
- Ability to proactively drive initiatives with minimal oversight. You understand when needs arise and creatively find solutions.
- Demonstrated interest in implementing agentic AI in a data context. Actual experience preferred, but optional.
More about Givebutter
Benefits
- Remote Work: Work remotely from one of our 10 hubs (Austin, Denver, Indianapolis, Los Angeles, San Francisco, New York, Salt Lake City, Minneapolis, Seattle, and Nashville).
- Health Insurance: We offer Medical, Dental, and Vision insurance covered 100% for employees as well as HSA and FSA accounts.
- Dependent Care Coverage: We offer coverage for dependents, with 50% of Medical, Dental, and Vision premiums covered for all eligible dependents.
- Mental Health: Givebutter health insurance plans come with access to a TalkSpace membership.
- 401k: We offer a 3% 401k match for all eligible employee's.
- Vacation and Holidays: Givebutter offers a Flexible PTO policy with uncapped vacation days and company-recognized holidays.
- Wellness Week: Givebutter closes for one week each summer to prioritize rest and recharge for the entire team.
- Parental Leave: We offer 12 weeks of paid leave for all parents and comprehensive leave planning management through Aidora.
- Family Care Support: Access a company-paid UrbanSitter membership plus care credits to book trusted, background-checked caregivers for childcare, senior care, pet care, and household support when you need it most.
- Home Office Stipend: Upgrade your home office with company-sponsored expenses, including high-quality laptops, monitors, and modern technology.
- Coworking Stipend: Enjoy a monthly stipend that gives you the freedom to work from coworking spaces or cafés whenever you need connection, community, or a change of scenery.
- Charitable Giving: Employees are encouraged to donate up to $50/month to any verified nonprofit they wish to support on Givebutter.
- Professional Development: We offer learning and development reimbursement opportunities.
- Love What You Do: We are a mission-driven company serving the charitable sector. Feel good about the work you're doing and the company you work for.
Interview Process
Below is a high-level outline of our standard interview process
- Recruiter Screen: A 30-minute conversation to learn more about your background, walk through the role, and ensure mutual alignment on expectations, values, and logistics.
- Hiring Manager Interview: A deeper dive into your relevant experience, skillset, and working style. This is your first opportunity to connect directly with the person who may be your future manager.
- Assessment (technical or non-technical): This stage will vary based on the role. It could involve a live coding session, case study, or take-home project. Some roles may include two parts to this stage to evaluate both practical skills and problem-solving approaches
- Values Interview: A conversation with team members focused on how you align with our core values and leadership principles.
- References: We connect with a few folks you’ve worked closely with to get a better picture of your working style and impact.
- Offer: If all goes well, we’ll move to the offer stage!
Please note, we will have an AI note-taking tool join most of our interviews.
Hi potential new butterslice! A recent study from LinkedIn showed that most women apply to jobs only when they meet 100% of the requirements, whereas men will hit the apply button if they hit 60%. Givebutter is committed to building a diverse and inclusive team. So to the women and nonbinary folks out there feeling unsure if you're a perfect fit, we strongly encourage you to apply!
Compensation Range: $120K - $140K
Salary Context
This $120K-$140K range is in the lower quartile for Data Scientist roles in our dataset (median: $167K across 150 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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Givebutter, 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 $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($130K) sits 32% below the category median. Disclosed range: $120K to $140K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Givebutter AI Hiring
Givebutter has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $140K - $140K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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