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 Unsecured Underwriting Machine Learning team develops and improves the models that inform credit decisions for our unsecured lending products. The team conducts machine learning research, evaluates model performance, and partners closely with engineering teams to translate promising ideas into scalable model improvements.

As an Applied Scientist, you will research and implement enhancements to Upstart’s core unsecured underwriting model. Your work will directly influence credit decisioning and help the team increase the pace of research while maintaining the rigor, reliability, and responsible oversight required of production underwriting models.

As an Applied Scientist in the Unsecured Underwriting Machine Learning team at Upstart, you will:

Research machine learning and statistical approaches that improve the predictive performance of unsecured underwriting models.

Design, implement, and evaluate model enhancements using rigorous experimentation and validation methods.

Analyze model performance and downstream effects to confirm that proposed changes improve decisioning reliably.

Partner with various engineering teams to support technical reviews, implementation, and deployment.

Translate research findings into clear recommendations, documented methodologies, and production-ready solutions.

Minimum Qualifications

Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.

0–2 years of experience conducting machine learning, statistical modeling, or applied quantitative research in an academic or industry setting.

Experience developing and evaluating machine learning or statistical models using Python.

Demonstrated knowledge of probability, statistics, and machine learning methods.

Experience designing experiments or validation analyses to assess model accuracy, reliability, or predictive performance.

Preferred Qualifications

PhD in a quantitative field.

Knowledge of supervised learning, model evaluation, and feature engineering techniques.

Experience working with large or complex datasets and translating research concepts into implemented solutions.