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 develops and advances predictive models that help make lending more accessible and efficient. Through applied research, experimentation, and close collaboration with engineering and business partners, the team turns complex data into reliable machine learning solutions.

As a Senior Applied Scientist at Upstart, you will lead the development of machine learning solutions that improve model performance and business outcomes. You will own complex modeling problems from initial investigation through production implementation, while helping shape technical approaches and scientific best practices.

How you’ll make an impact

Lead end-to-end development of machine learning models, from problem formulation and experimentation through deployment and performance evaluation.
Design and evaluate advanced modeling approaches to improve predictive accuracy, robustness, and operational effectiveness.
Identify opportunities to improve existing models through feature development, experimentation, and rigorous statistical analysis.
Partner with engineering, product, and business stakeholders to translate complex requirements into measurable machine learning objectives.
Guide technical decisions, review scientific methodologies, and share expertise to improve the quality and effectiveness of the team's work.

Minimum Qualifications

Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent practical experience.
3 years of experience developing and evaluating machine learning models or applying statistical methods to production-oriented problems.
Experience developing machine learning solutions using Python and established machine learning frameworks.
Experience designing and evaluating experiments using statistical methods and quantitative performance metrics.
Experience implementing, deploying, or maintaining machine learning models in production environments.

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 methods, including ensemble learning, deep learning, or probabilistic modeling.
Ability to evaluate tradeoffs among model performance, interpretability, reliability, and computational efficiency.
Experience with large-scale data processing or distributed machine learning systems.