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 applies machine learning and statistical methods to improve how we evaluate risk and make lending decisions. The team combines scientific research, experimentation, and engineering to develop models that support Upstart’s mission of expanding access to affordable credit.

As an Applied Scientist at Upstart, you will develop and evaluate machine learning models that address meaningful business problems. You will collaborate with scientists, engineers, and cross-functional partners to translate complex data into modeling improvements and measurable outcomes.

How you’ll make an impact

Develop, train, and evaluate machine learning models to improve predictive performance and support lending decisions.
Analyze large datasets to identify patterns, develop features, and inform modeling approaches.
Design and execute experiments to assess model performance, validate hypotheses, and identify opportunities for improvement.
Collaborate with scientists and engineers to implement, monitor, and refine models in production environments.
Document methodologies, communicate findings, and contribute to reproducible scientific workflows.

Minimum Qualifications

Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent practical experience.
0–2 years of experience in machine learning, applied statistics, or a related quantitative field.
Experience developing or evaluating machine learning models using Python.
Experience applying statistical methods, including hypothesis testing, regression, or predictive modeling, to quantitative datasets.

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 supervised learning techniques, model evaluation, and feature engineering.
Familiarity with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
Experience working with large-scale datasets using SQL or distributed data processing tools.
Ability to communicate modeling approaches and experimental results to technical and nontechnical audiences.

Position location This role is available in the following locations: Remote

Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.