**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 (SMBs) to manage cash flow. At the heart of this mission, the QuickBooks Lending 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 the Lending Marketplace—a strategic growth initiative focused on connecting small and medium businesses with the most suitable loans from external lenders. By leveraging Intuit’s rich customer data, this platform aims to deliver personalized loan offers and streamline the application experience. In this role, you’ll analyze both internal and lender data to uncover actionable insights, provide strategic recommendations, and help scale the marketplace 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 in fintech lending including credit card or marketplace analytics is a plus. **What you'll bring** We’re looking for a curious, proactive data scientist with a passion for fintech. + BS or MS in Statistics, Mathematics, Operations Research, Computer Science, Engineering, Econometrics, or a related field. + 4+ years of experience in data science and analytics in the fintech sector, ideally in lending, credit cards, or marketplaces. + Strong expertise in predictive modeling, customer segmentation, and experimentation design. + Proven ability to form hypotheses based on customer behavior, industry trends, and market conditions. + Demonstrated success designing and interpreting complex experiments beyond traditional A/B testing. + Experience in building scalable, reusable analytics tools and avoiding redundant efforts. + Excellent communication and stakeholder influence skills across business and technical teams. + Ability to work independently and collaboratively in a fast-paced, dynamic environment. Technical Skills: + Advanced SQL proficiency and hands-on experience with visualization tools such as Qlik, Tableau, or Plotly Dash. + Strong analytical and modeling skills using Python (with libraries such as numpy, pandas, scikit-learn, etc.). + Experience applying statistical and machine learning techniques to solve go-to-market and marketing problems (e.g., feature adoption propensity, churn risk scoring, next best action models). 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 and marketplace dynamics. + Familiarity with Generative AI and other emerging technologies to accelerate insights from multimodal data sources. **How you will lead** As a Senior 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 the future of Intuit’s Lending Marketplace and positively impact millions of small and medium sized businesses + Developing predictive models, conducting experiments beyond A/B testing, and uncovering 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, Credit Underwriting, Engineering, Design, and Analytics 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.