Staff Data Scientist, Lending Externalization

Intuit • Mountain View, CA • 4h ago

Overview

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 an experienced Data Scientist to lead data science and analytics for our partnerships and externalization efforts -a strategic growth initiative focused on connecting small and medium businesses with the most suitable loans. By leveraging Intuit's rich customer data, this platform aims to partner with other organizations like Amazon to deliver personalized loan offers.

In this role, you'll analyze both internal and lender data to uncover actionable insights, provide strategic recommendations, and help scale our partnerships and externalization efforts rapidly. The ideal candidate will have a solid foundation in quantitative analysis, experience working with large datasets, and a strong background in data-driven decision-making. Prior experience with partnerships-focused analytics or data science is a plus.

What you'll bring

We're looking for a curious, proactive data scientist with a passion for Fintech along with:

6-10 years of experience in data science and analytics

Proven ability to form hypotheses and find business opportunities based on customer behavior, industry trends, and market conditions

Experience in building scalable, reusable analytics tools and avoiding redundant efforts

Demonstrated success designing and interpreting complex experiments beyond traditional A/B testing

Strong expertise in causal inference, predictive modeling, customer segmentation, and experimentation design.

Excellent communication and stakeholder influence skills across business and technical teams

Ability to work independently and collaboratively in a fast-paced, dynamic environment

BS or MS in Statistics, Mathematics, Operations Research, Computer Science, Engineering, Econometrics, or a related field

Technical Skills:

Advanced SQL proficiency and hands-on experience with visualization tools such as Tableau

Strong analytical and modeling skills using Python or R

Experience applying statistical and machine learning techniques to solve go-to-market and product problems

Preferred Additional Qualifications

Expertise in causal inference, LTV modeling, or other statistics in situations where A/B testing ability is limited

Background or experience working in analytics engineering or business intelligence roles

Experience addressing growth-related challenges at fintech companies focused on lending or fintech partnerships serving consumers or SMBs

Familiarity with Generative AI and other emerging technologies

How you will lead

As a Staff Data Scientist on the Lending Data Science team, you will play a pivotal role in

shaping strategy through deep analytical insights. This includes:

Conceptualizing business opportunities, formulating hypotheses, defining goals and key metrics, and delivering actionable recommendations.

Driving strategic insights to shape growth for QuickBooks Capital and positively impact millions of small and medium sized businesses

Developing predictive models, causal inference studies, and A/B testing to uncover customer insights to drive product, marketing, and lending innovations.

Creating durable customer segmentation strategies to enhance targeting, positioning, and user experience.

Collaborating closely with cross-functional partners in Product Management,

Marketing, Engineering, and Design to inform and guide product strategy.

Translating complex data into clear, actionable insights for both technical and non-technical stakeholders

EOE AA M/F/Vet/Disability. Intuit will consider for employment qualified applicants with criminal histories in a manner consistent with requirements of local law.

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