Applied Scientist

About Upstart At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence. As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress. We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City, you’ll have the support to work in the way that works best for you. If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you. The Team Upstart’s Partnerships team helps drive customer acquisition and originations through partner offers. The team is focused on improving how offers are optimized across the customer journey. As the Applied Scientist on the Partnerships team, you’ll use causal inference, experimentation, and machine learning to help determine which offers are most likely to support customer conversion. You’ll take on a greenfield optimization opportunity, moving from research and measurement to production-ready models and decision strategies.

How you’ll make an impact - Develop causal machine-learning models to optimize customer offers at different points in the funnel, from marketing-time decisions with limited information to decisions informed by customer activity on our site. - Analyze and interpret complex data, identify gaps in what we know, and design experiments to gather evidence for building and evaluating optimization models. - Measure the impact of offer changes with statistically rigorous experiments and evaluations. - Translate research and results into clear recommendations, decision strategies, and next steps for business and technical partners. - Build production-ready models and code for use in offer selection. - Partner with Partnerships leadership, Product, Engineering, peer machine-learning teams, and model-risk partners to align on strategy, implementation, and methodology. Minimum Qualifications - Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field. - 2+ years of experience conducting machine-learning, statistical-modeling, or applied quantitative research in an academic or industry setting. - Experience applying causal inference methods and experimentation to real-world decisions in an applied-science or data-science context. - Applied statistics, probability, and experimental-design expertise used to build or evaluate models or interventions. - Production-quality coding experience in Python, including data manipulation and algorithm implementation. Preferred Qualifications - PhD in a quantitative field. - Experience with marketplace optimization, particularly where pricing or offer decisions are central. - Experience with causal machine-learning methods or treatment-effect modeling.