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 Machine Learning team advances the scientific methods and predictive systems that power our AI-driven lending marketplace. The team combines applied research, large-scale modeling, and cross-functional collaboration to improve decision quality, model reliability, and the effectiveness of our lending platform.

As a Staff Applied Scientist at Upstart, you will provide technical leadership across complex, high-impact machine learning initiatives. You will define scientific approaches to ambiguous problems, influence modeling strategy across teams, and establish methods that improve the performance and reliability of our machine learning systems.

How you’ll make an impact

Define technical direction for complex machine learning initiatives, translating broad business objectives into scientific strategies and measurable outcomes.
Lead the design and evaluation of advanced modeling approaches that address challenging problems in predictive performance, robustness, and scalability.
Identify systemic opportunities to improve model development, experimentation, evaluation, and production performance across teams.
Partner with scientific, engineering, and business leaders to influence technical priorities and guide decisions involving complex modeling tradeoffs.
Establish scientific best practices, provide technical mentorship, and raise the quality and consistency of machine learning work across the organization.

Minimum Qualifications

Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent practical experience.
4 years of experience developing machine learning models or applying advanced statistical methods to complex quantitative problems.
Experience leading the technical design and implementation of machine learning solutions used in production environments.
Experience applying advanced machine learning methods and statistical evaluation techniques to large-scale datasets.
Experience defining technical approaches or architecture for machine learning initiatives involving multiple stakeholders or systems.

Preferred Qualifications

Advanced degree, ideally a PhD, in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.
Knowledge of advanced machine learning techniques, including deep learning, ensemble methods, or probabilistic modeling.
Ability to evaluate complex tradeoffs involving predictive performance, interpretability, scalability, and model reliability.