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 Machine Learning Growth team develops models that help optimize borrower acquisition across marketing channels. The Direct Mail team focuses on causal machine learning models that predict incremental conversion and help prioritize prospects, and its scope is expanding beyond Personal Loans into Home Equity Line of Credit (HELOC), email marketing, digital, and other marketing use cases.

The Role:

As an Applied Scientist at Upstart, you will improve existing models and develop new approaches that expand the team’s impact across marketing channels. You will work across business-oriented analysis, machine learning research, experimentation, and production-ready modeling, partnering primarily within Machine Learning and with Growth and Marketing Platform Engineering stakeholders.

How you’ll make an impact:

Analyze historical model and campaign performance to identify opportunities to improve model effectiveness and marketing outcomes.

Develop and evaluate machine learning models, including researching new features and model architectures, to improve prospect selection and support new marketing use cases.

Design statistically rigorous experiments and model evaluations to measure causal impact, optimize campaign outcomes, and inform decisions.

Navigate complex, messy datasets and build reusable data pipelines, metrics, and analytical approaches that enable efficient model development and analysis.

Partner with Machine Learning, Growth, and Marketing Platform Engineering teams to translate business problems into research agendas, intermediate milestones, and production-ready solutions.

Expand machine learning capabilities beyond direct mail into lifecycle, email, digital, and other emerging marketing channels.

Minimum Qualifications:

Master’s Degree in Mathematics, Statistics, Economics, Operations Research or a related field

Experience applying statistical and machine learning methods to modeling or data science problems.

Experience using Python for data analysis, data preparation, and machine learning model development.

Experience with causal inference and experimental design, including statistically rigorous evaluation of model or experiment performance

Preferred Qualifications:

PhD in Mathematics, Statistics, Economics, Operations Research or a related field (or its equivalent)

Knowledge of causal machine learning methods and modeling approaches.