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About This Role
Overview
Intuit's Global Business Solutions Group (GBSG) builds the platform that powers prosperity for millions of solo, small and mid-market businesses. Workforce Solutions — QuickBooks Payroll, Time, Benefits, and Contractor Payments — is how that platform serves the people those businesses employ, and it is one of the fastest-growing, highest-retention parts of the QuickBooks ecosystem.
We are looking for a curious, influential, and deeply strategic data scientist to set the data science direction for Workforce Solutions and accelerate its growth.
This is a principal-level individual contributor role. You will operate across the entire Workforce portfolio — partnering directly with Product, Product Marketing, Design, Engineering, and Finance leaders — and be accountable for the data-backed point of view that shapes the multi-year Workforce roadmap. You will define how we measure growth for this business, decide which questions are worth answering, and bring the rigor that turns ambiguous strategic bets into testable, sized hypotheses. Success looks like Director and VP leaders changing their plans because of the work you put in front of them.
Responsibilities
Own the data science strategy for Workforce Solutions end to end: decompose the growth thesis for payroll, time, and benefits into the measurable drivers underneath it, identify the critical assumptions in the plan, size their sensitivity, and design the measurement that resolves them
Drive attach, activation, and retention growth across the Workforce portfolio through deep analysis of customer behavior — from the moment a QuickBooks customer hires their first employee through run-rate payroll, time tracking, and benefits adoption
Set the experimentation bar for the business unit: design and adjudicate high-stakes tests, apply causal methods where clean experiments aren't possible (quasi-experimental designs, synthetic control, difference-in-differences, uplift modeling), and establish the standards other data scientists build against
Define and own the metric system for Workforce Solutions — the canonical definitions of attach, seat growth, engagement, and retention that Product, Finance, and leadership all plan against
Synthesize business performance narratives for the portfolio, clearly articulating the unified story on growth drivers, headwinds, and the actions that move them
Build durable understanding of Workforce customer segments and their path to growth, translating it into a prioritized set of product and go-to-market bets
Own the data architecture and instrumentation that power clean views on Workforce performance; extend it with new data sources and technology integrations, increasingly powered by agentic systems
Raise the craft of data science across the organization — mentoring senior and staff data scientists, setting technical standards, and multiplying the impact of the broader team without formal management responsibility
Qualifications
10+ years in data science or analytics with a track record of setting strategy and driving outcomes at the business-unit level, across multiple initiatives and partner organizations
Deep expertise in causal inference and experimentation: experimental design and analysis at scale, plus fluency in quasi-experimental and observational causal methods for the many cases where a clean A/B test isn't possible
Proven ability to direct product and growth roadmaps and influence strategy with data-backed hypotheses — not just to answer questions, but to decide which questions matter
Exceptional tops-down executive communication: proven ability to construct a data narrative in dynamic, ambiguous situations and hold the room with Director, VP, and SVP-level leaders
Ability to partner with XFN leaders and advocate for DS programs with significant downstream dependencies; owns prioritization and execution across a broad portfolio
Deep strategic and business management thinking, with working knowledge of subscription growth, unit economics, and product-led growth dynamics
Advanced SQL and Python, hands-on instrumentation and pipeline/ETL construction, and comfort working directly in large, multi-source enterprise data
Demonstrated ability to mentor and elevate senior data scientists and set technical standards for a team
Experience in payroll, HR technology, fintech, or another compliance-heavy domain is a plus; experience building and shipping AI agent systems is a plus
BS or MS in Statistics, Mathematics, Economics, Econometrics, Operations Research, Computer Science, Engineering, Business or a related quantitative field, or equivalent work experience. Advanced degree preferred.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
The expected base pay range for this position is:
Mountain View $247,500 - $335,000
San Diego, CA $241,000- $326,500
Salary Context
This $247K-$335K range is above the 75th percentile for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Intuit, 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($291K) sits 53% above the category median. Disclosed range: $247K to $335K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Intuit AI Hiring
Intuit has 32 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer, MLOps Engineer. Positions span Mountain View, CA, US, San Diego, CA, US, New York, NY. Compensation range: $116K - $369K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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