Senior Applied Scientist

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.

Upstart’s Servicing team supports borrowers after loan origination and works to improve outcomes for investors and borrowers. The team is building data-informed approaches across areas such as collections, loan assistance, payment experiences, and borrower communications, with meaningful opportunity to apply machine learning to problems that have often been addressed manually.

As a Senior Applied Scientist on the Servicing team, you will build predictive and causal models and translate their outputs into decisions that improve how loans are serviced. You’ll partner with Product, Engineering, and Servicing and Collections Operations to evaluate interventions and shape data-informed approaches to payment plans, loan modifications, communications, and autopay.

• Develop and evaluate machine-learning models that predict borrower repayment behavior and other servicing outcomes.
• Apply causal inference and experimentation to measure the impact of servicing interventions, including loan modifications and payment assistance.
• Translate model predictions and treatment-effect estimates into practical decision algorithms and actions.
• Partner with Product and Engineering on payment experiences, autopay, and borrower communications, and with Servicing and Collections Operations on collections strategy and agent-performance insights.
• Identify and develop machine-learning opportunities in servicing areas that have historically relied on manual optimization.
• Explain analytical choices, tradeoffs, and findings to technical and non-technical partners to inform high-stakes operational and product decisions.

• Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.
• 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.
• 3+ years of…