Interested in this Data Scientist role at Givelify?
Apply Now →Skills & Technologies
About This Role
Senior Data Scientist
Givelify is a fintech\-for\-good company where brilliant minds come to power the most loved and trusted online and mobile giving app platform. Thanks to over 2 million generous donors, we've helped more than 80,000 nonprofits and places of worship raise over $7\.5 billion. Together, they're changing their world with kindness and generosity.
Inc. 5000, the Stevies® Awards, Gartner, Forbes, and many more have recognized our story, innovations, and achievements. Our award\-winning team builds products and experiences that put more good into the world. We love to take on big challenges because we know our work matters.
Make an impact at one of the fastest\-growing private companies in the U.S. Be part of a talented team of big\-hearted individuals, earning competitive pay with excellent benefits.
About your role
We're seeking a Senior Data Scientist to be an early and foundational member of a growing Data Science \& Insights team — with a focus spanning product and business analytics at Givelify. Reporting to the Head of Data Science \& Insights, you will work across the full analytics stack — dashboards, metrics, experimentation, and modeling — with an immediate focus on building the business intelligence infrastructure that gives Givelify transparent, reliable visibility into how the organization is performing.
This is a generalist senior individual contributor role by design. As the team grows and specialization becomes possible, your focus will evolve with it. For now, breadth is the asset — you are someone who can move fluidly across problem types and deliver rigorously wherever you land.
Your Team
You will be part of the Data Science \& Insights team — driving experimentation, analytics, and modeling at the frontier of behavioral science and fintech, tackling problems most data teams never encounter. We translate the complexity of human generosity into rigorous, measurable insight that shapes products and decisions at scale.
Senior Data Scientist will get to:
- Own the development of org\-wide business dashboards that provide transparent, reliable insight into company performance, functional health, and execution velocity — the infrastructure that helps Givelify understand how it is operating at any moment.
- Partner with cross\-functional teams to define, instrument, and maintain the metrics that matter — ensuring the right things are being measured, consistently and trustworthy.
- Partner with Product and cross\-functional teams to design and execute rigorous A/B and multivariate experiments that establish causation before solutions launch — embedding an experiment\-first mindset into how Givelify builds and decides.
- Work alongside the Data Science \& Insights team to continuously raise the bar on experimentation at Givelify — tackling hard methodological challenges like measuring the impact of experience\-driven features, managing concurrent experiments, and developing approaches that make rigorous testing possible even where it has historically been difficult.
- Build predictive and behavioral models that deepen understanding of donor and organizational behavior — segmentation, churn, propensity, and beyond.
- Translate complex analytical findings into clear, decision\-ready recommendations for executives and cross\-functional stakeholders.
To be successful in this role, you'll also:
- Own your outcomes completely — you hold yourself accountable for scoping, delivery, and impact, and you execute collaboratively, knowing the best analytical work at Givelify is built with teams, not in isolation.
- Proactively surface opportunities to bring experimentation and causal rigor to decisions across the organization — making the experiment\-first mindset contagious.
- Bring stakeholders into the analytical process early and continuously — from question definition through to recommendation — so that insights translate into action.
- Lead with expertise and invest in others — as an early member of a growing function, your impact extends beyond your own output to how the team gets better.
Your experience:
- 5–8\+ years of experience in data science, business intelligence, applied statistics, or advanced analytics
- Master's or Ph.D. in Statistics, Data Science, Operations Research, Mathematics, Econometrics, or a related quantitative field
- Strong track record building business dashboards and insight systems that drive operational visibility and executive decision\-making
- Solid experience with experimental design and causal inference — A/B testing, power analysis, difference\-in\-differences, propensity methods — and translating results into business decisions
- Strong background in predictive and behavioral modeling — regression, classification, segmentation, time series, gradient boosting — with a focus on business application
- Expert in SQL; advanced proficiency in Python (pandas, scikit\-learn, statsmodels, PySpark) and/or R
- Hands\-on experience with modern data stacks, Databricks preferred; broader fluency with modern data stacks essentials
- Comfortable working across problem types — you don't need a narrow lane to do excellent work
- Practical fluency using AI/LLM tools to accelerate analytical work (e.g., exploratory analysis, code assistance, documentation), along with an understanding of responsible AI use and data governance
Your superpowers:
- You influence through evidence and storytelling — you don't just analyze; you move people and decisions with what you find
- You translate ambiguity into clarity — where others see a messy question, you see an analytical path forward
- You are data\-informed, not data\-driven — you know that data illuminates decisions, and you bring analytical rigor and human judgment in equal measure
- You are a custodian and evangelist of causal rigor — you champion well\-designed, preemptive experiments as the gold standard for knowing what works, and you carry that conviction into every analytical conversation
- You are a player\-coach — you lead with expertise, build strong processes, and make the people around you better
- You are energized by being early — you find building more exciting than inheriting a mature function
- You are comfortable with a shifting focus — you thrive when priorities evolve and don't need a narrow lane to do excellent work
Our culture:
We are a virtual team of award\-winning and high\-performing professionals who innovate and collaborate to fulfill our mission to instantly connect people to causes that matter most to them so they can change their world with kindness and generosity. Our four keys to success \- integrity, heart, simplicity, and wow \- fuel our passion to be recognized among the tech industry's most inclusive and purpose\-driven workplaces.
We are steadfast in our conviction to overcome challenges while performing meaningful work alongside some of the most brilliant minds and biggest hearts. It propels our growth as individuals and as a team. We are committed to respecting each other in our workplace, firmly believing diversity is our strength.
At the heart of everything we do are our giving community, our donors and our partner organizations. We lean on research and human\-centered design to consistently push the envelope and innovate our products and customer experiences.
We take great pride in providing competitive pay, full benefits to help care for you today and in the future, amazing perks (including flexible PTO), and, most importantly, the opportunity to put passion and purpose front and center.
About Givelify
Givelify is the most loved and trusted online and mobile giving platform. Along with its powerful donation management system, it's the fastest\-growing technology for advancing generosity in the world. We instantly connect people to their heart's impulse to do good with award\-winning products and experiences. A global community of over 2 million generous people supports their favorite churches, places of worship, nonprofits, and causes with over $7\.5 billion in donations across more than 80,000 organizations. Givelify leads all giving apps on the App Store and Google Play Store with more than 135,000 verified, authentic reviews with an average 4\.9 out of 5\-star rating. Learn more at Givelify.com.
Ready to join the Givelify team? Apply below.
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Givelify, 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 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Givelify AI Hiring
Givelify has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.