Applied Scientist Intern

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...

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 s...

As an Applied Scientist Intern, you will become a specialist in our models, products, business and/or codebase. You will identify potential problems or opportunities for improvement, communicate these to the team and suggest solutions. This involves self-directed investigation, offline analyses using creative thinking as well as presentation to the ML team. You will receive mentorship from experienced ML practitioners and collaborate closely with the team to improve our products and models.
Research machine learning and statistical approaches to discover improvements to the predictive performance and/or business value of a model.
Design, implement, and evaluate model, product, or technical enhancements using rigorous experimentation and validation methods.
Translate research findings into clear recommendations, documented methodologies, and production-ready solutions.
Work with your mentor to align stakeholders, and drive next steps.

Bachelor. Masters or PHD degree in mathematics, statistics, physics, econometrics, computer science, or a related quantitative field. On track to graduate by 2028.
Strong fundamentals in statistical, probability, and machine learning theory.
Ability to perform data analysis and evaluate machine learning or statistical models using Python, as well as basic SQL foundations.
Intellectual curiosity, humility, drive and teamwork.
Strong communication skills.
Ability to use modern agentic tooling (Claude Code, Codex, …) efficiently.
Phd in mathematics, statistics, physics, econometrics, computer science, or a related quantitative field. On track to graduate by the summer of 2028.
Experience in supervised learning, model evaluation, and feature engineering techniques.
Advanced statistical (frequentist, bayesian, causal) theory/econometrics knowledge.
Strong coding skills in Python.