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About This Role
Overview
Intuit’s Global Business Solutions Group (GBSG) is dedicated to creating tools and services that dramatically increases the ability of small-medium businesses (SMBs) to manage cash flow. At the center of this mission, the QuickBooks Money team is developing innovative tools to help customers get paid faster and with confidence. Our expanding suite of offerings and new go-to-market strategies aim to further enhance SMBs' ability to accept payments seamlessly.
The Money Data Science team on the GBSG Data & Analytics organization’s mission is to deliver delightful customer experiences and drive business impact by developing insights, models, and strategic thought partnership that helps small and mid-market business customers on managing their money. We partner with Product Management, Commercial, Finance, Business Operations, Design and Engineering teams to foster data-driven decisions through strategic thinking, data analysis, experimentation, and predictive analytics.
As the founding Senior Staff Data Scientist working on an accelerating growth for a strategic priority within the company, you will dive deep into our data to uncover actionable insights and make recommendations that will fuel growth of this new product. The ideal candidate will have a strong background in quantitative analysis using large data sets, demonstrated growth hacking experience on mature and high growth products, and in driving influence and alignment with data.
Responsibilities
As a Senior Staff Data Scientist on the Product Data Science team, you will dive deep into our data to uncover actionable insights and shape strategic decisions. Your contributions will directly impact our product development and GTM strategies of a brand new product and strategic bet. This role requires a strong background in quantitative analysis, data-driven decision-making, and expertise in working with large data sets.
Conceptualize business problems or opportunities, formulate hypotheses and goals, define key metrics and make actionable recommendations
Drive strategic insights that shape the future of Money-movement ecosystem products within Intuit, impacting millions of small businesses
Develop predictive models, conduct experimentation beyond A/B testing, and generate actionable customer insights that inform product innovation
Build and apply durable customer segmentation patterns to renew product targeting, positioning, and customer experience
Partner closely with Product Management, Marketing, Engineering, Design, and Analytics leaders to deliver insights that drive product strategy and growth
Translate complex data insights into actionable recommendations for technical and non-technical stakeholders, and business leaders
Qualifications
The ideal candidate is a curious, proactive data scientist with experience in building scalable solutions, a strong understanding of customer behavior, and a passion for fintech.
BS or MS degree in Statistics, Mathematics, Operations Research, Computer Science, Econometrics or related field. Equivalent experience will be considered.
10+ years of experience in data science or product analytics, preferably in fintech, with a strong foundation in predictive modeling, customer segmentation, and experimentation
Ability to formulate data-backed strategies that will drive step-function growth for the business as well as increase customer benefit
Ability to generate hypotheses grounded in customer behavior, industry trends, and external market factors. Experience in the fintech or SMB space is highly preferred.
Experience in designing and interpreting complex experiments beyond traditional A/B testing methods
Demonstrated experience in building reusable and scalable analytics solutions, with a focus on efficiency and avoiding duplication of work
Outstanding communication skills with the ability to influence decision makers and build consensus with teams
Quick learner, adaptable, with the ability to work independently or as part of a team in a fast-paced environment
Experience using statistics and machine learning techniques to solve complex business problems within go-to-market and marketing areas, e.g., propensity for feature adoption, customer health scoring to identify customers at risk to cancel, next best action models etc.
Technical Skills:
Advanced SQL skills and proficiency in visualization tools such as Qlik, Tableau, Plotly Dash
Strong analytical and modeling skills using Python (for its rich suite of statistical and modeling libraries like numpy, pandas, scikit-learn, etc.)
Familiarity with Linux/OS X command line, version control software (git), and general software development
Preferred Additional Qualifications
Experience solving growth-related problems at financial technology companies serving consumer or SMBs
Familiarity with Generative AI and other evolving technologies to accelerate insights from multi-modal data
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 $210,500 - $284,500
Mountain View, CA $210,500- $284,500
Salary Context
This $210K-$284K 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 ($247K) sits 30% above the category median. Disclosed range: $210K to $284K.
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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