Job description

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. 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). Technical Skills Advanced SQL proficiency and hands-on experience with visualization tools such as Tableau. Strong analytical and modeling skills in Python or R, plus fluency with data workflow management (authoring, scheduling, monitoring). Deep experience applying statistical and machine learning techniques to go-to-market, product, and lending problems. Hands on experience using and architecting generative AI workflows using tools such as Claude Code, Codex, etc Preferred Additional Qualifications Experience addressing growth-related challenges at fintech companies focused on lending, credit cards, or marketplaces serving consumers or SMBs. Understanding of lending product nuances (underwriting, credit risk, LTV modeling). Working knowledge of AI model performance metrics (cost, latency, accuracy, hallucination) and the AI development lifecycle. 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 San Francisco Howard Street, CA $210,500- $284,500

Requirements

Must-haves

We're looking for a curious, proactive, and influential data science leader with a passion for fintech.
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.
Technical Skills
Advanced SQL proficiency and hands-on experience with visualization tools such as Tableau.
Strong analytical and modeling skills in Python or R, plus fluency with data workflow management (authoring, scheduling, monitoring).
Deep experience applying statistical and machine learning techniques to go-to-market, product, and lending problems.
Hands on experience using and architecting generative AI workflows using tools such as Claude Code, Codex, etc
Experience addressing growth-related challenges at fintech companies focused on lending, credit cards, or marketplaces serving consumers or SMBs.
Understanding of lending product nuances (underwriting, credit risk, LTV modeling).
Working knowledge of AI model performance metrics (cost, latency, accuracy, hallucination) and the AI development lifecycle.
Mountain View $210,500 - $284,500
San Francisco Howard Street, CA $210,500- $284,500

Nice-to-haves

BS or MS in Statistics, Mathematics, Operations Research, Computer Science, Engineering, Econometrics, or a related field (advanced degree preferred).
Preferred Additional Qualifications

Application prep

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Keywords to mirror

mlaisqlpythonmachine learninggo

Platform: career-feeds
Effort: medium

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