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Overview
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Intuit's Global Business Solutions Group (GBSG) is committed to building tools and services that significantly enhance the ability of small and medium\-sized businesses to manage cash flow. At the heart of this mission, the QuickBooks Capital team is developing innovative solutions that empower customers to confidently access the right loan offerings with greater ease.
The Lending Data Science team is seeking a Sr. Staff Data Scientist to serve as the analytical leader and strategic thought partner across our lending portfolio — spanning the Lending Marketplace (connecting small and medium businesses with the most suitable loans) and our partnerships \& externalization efforts (e.g., partnering with organizations like Amazon to deliver personalized loan offers at scale). This is a high\-impact, cross\-initiative role where you will set the analytics vision, raise the scientific bar across the team, and influence product, marketing, and lending strategy at the Business Unit level.
As a Sr. Staff Data Scientist, you operate as a technical leader and domain expert across multiple teams and initiatives. You apply first\-principles thinking to turn business strategy into analytical problems, build reusable frameworks and methodologies that the broader analytics community adopts, and influence senior cross\-functional leaders (Directors and VPs) with insights grounded in deep customer understanding, business acumen, and industry\-wide context. Responsibilities
- Set strategy across initiatives: Turn QuickBooks Capital's business strategy into analytical problems across multiple initiatives (Marketplace and partnerships/externalization), iteratively self\-generating and validating hypotheses to create actionable insights and recommendations that inform decision\-making at the Business Unit level.
- Influence senior leadership: Combine insights, business acumen, strategic considerations, and industry\-wide learnings to influence cross\-functional leaders up to the VP level; act as the connective tissue across Product, Marketing, Engineering, and Design.
- Advance the science: Identify new ML and causal inference methodologies and external trends, adapt them to lending use cases, and create shareable frameworks that enable adoption across the BU — with clarity on when and how each methodology should be used to drive business value.
- Lead experimentation at scale: Drive an iterative experimentation culture across the team — designing complex experiments (A/B/n, painted\-door, bandits, geo/holdout, and quasi\-experimental designs) and applying causal inference (Propensity Score, DiD, Synthetic Control, with growing depth in Doubly Robust Estimation and Instrumental Variables) where A/B testing is limited.
- Build durable segmentation \& customer understanding: Identify key patterns in customer behavior by connecting insights across a portfolio of experiments and analyses; create durable customer segmentation strategies that enhance targeting, positioning, and the application experience.
+ Shape the AI\-native roadmap: Co\-create the analytics/AI strategy for lending in partnership with cross\-functional teams; guide phased testing and rollout with the right measurement, safety, risk, and ethical considerations; connect model performance metrics to customer and business outcomes.
+ Drive build/buy and tooling decisions: Identify the biggest pain points in analytics workflows and serve as a thought partner on build/buy decisions; champion reusable, scalable analytics tools that eliminate redundant effort across the team.
+ Raise the bar \& develop talent: Mentor and elevate Data Scientists across the team, set scientific standards and best practices, contribute to calibrations and hiring, and scale yourself through delegation while remaining hands\-on in the highest\-leverage areas.
Qualifications
We're looking for a curious, proactive, and influential data science leader with a passion for fintech.
- 9\+ years of experience in data science and analytics, with a track record of driving strategy and impact across multiple initiatives or business units; fintech experience (lending, credit cards, or marketplaces) strongly preferred.
- Demonstrated ability to apply first\-principles thinking to translate ambiguous business strategy into analytical problems at the business\-unit level.
- Proven success designing and interpreting complex experiments well beyond traditional A/B testing, and applying causal inference where experimentation is constrained.
- Deep expertise in predictive/prescriptive modeling, causal inference, customer segmentation, and experimentation design, with the judgment to balance statistical rigor and business considerations.
- Experience creating reusable frameworks, methodologies, and toolkits that are adopted by a broader analytics community.
- Exceptional communication and stakeholder\-influence skills, with a demonstrated ability to influence Director\- and VP\-level leaders across business and technical teams.
- Ability to navigate ambiguity with minimal guidance, make fast data\-driven decisions (one\-way vs. two\-way door), and operate effectively in a fast\-paced, dynamic environment.
- BS or MS in Statistics, Mathematics, Operations Research, Computer Science, Engineering, Econometrics, or a related field (advanced degree preferred).
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Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will 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
Salary Context
This $210K-$284K 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 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 in Demand for This Role
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 ($247K) sits 28% above the category median. Disclosed range: $210K to $284K.
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.
Intuit AI Hiring
Intuit has 10 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Product Manager. Positions span San Diego, CA, US, Mountain View, CA, US, New York, NY, US. Compensation range: $251K - $284K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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