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Staff Data Scientist, Funnel Analytics & Optimization

External
mulliganfunding logoMulliganfunding · San Francisco, CA
Full-timeHybrid5mo ago
Leadership
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About the role

Headquartered in San Diego, Mulligan Funding serves as a leading provider of working capital (Up to $5M) to the small and medium-sized businesses that fuel our country. Since 2008, we have prided ourselves on our collaborative, innovative, and customer-focused approach. Enjoying a period of unprecedented growth, driven by the combination of cutting-edge technology, human touch, and unwavering integrity, we are looking to add to our people first culture, with highly motivated and results-oriented professionals, to push the limits of what's possible while creating value for all of our partners. Mulligan Funding deploys a powerful combination of statistically grounded credit strategy and robust manual underwriting to deliver credit performance aligned with our risk appetite while providing an outstanding customer experience. The Staff Data Scientist, Funnel Analytics & Optimization is a key member of the Risk & Analytics team, responsible for analyzing end-to-end customer funnel performance and designing analytical frameworks that drive meaningful improvements in conversion, efficiency, and customer outcomes for a small-business portfolio. This role focuses on identifying friction points, drop-offs, and operational bottlenecks across the acquisition, underwriting, and application/funding funnel. The position partners closely with Risk leadership, Operations, Product, and Analytics teams to translate insights into actionable strategies that improve funnel conversion while maintaining strong risk discipline and customer experience. This role follows a hybrid work schedule with three days onsite and two days remote each week, with Mondays designated as a mandatory onsite day. Mulligan Funding is an Equal Opportunity Employer (EOE) and takes great pride in building a diverse work environment. Qualified applicants are considered for employment without regard to age, race, religion, gender, national origin, sexual orientation, disability or veteran status.


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