Staff Applied Scientist

The Team: 

Upstart’s Unsecured Underwriting Machine Learning team develops and improves the models that inform credit decisions for our unsecured lending products. The team conducts machine learning research, evaluates model performance, and partners closely with Engineering teams to translate promising ideas into scalable  model improvements.

As an Applied Scientist, you will research and implement enhancements to Upstart’s core unsecured underwriting model. Your work will directly influence credit decisioning and help the team increase the pace of research while maintaining the rigor, reliability, and responsible oversight required of production underwriting models.

As an Applied Scientist in the Unsecured Underwriting Machine Learning team at Upstart, you will:

• Research machine learning and statistical approaches that improve the predictive performance of unsecured underwriting models.
• Design, implement, and evaluate model enhancements using rigorous experimentation and validation methods.
• Analyze model performance and downstream effects to confirm that proposed changes improve decisioning reliably.
• Partner with various Engineering teams to support technical reviews, implementation, and deployment.
• Translate research findings into clear recommendations, documented methodologies, and production-ready solutions.

Minimum Qualifications 

• Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.
• 2+ years of experience conducting machine learning, statistical modeling, or applied quantitative research in an academic or industry setting.
• Experience developing and evaluating machine learning or statistical models using Python.
• Demonstrated knowledge of probability, statistics, and machine learning methods.
• Experience designing experiments or validation analyses to assess model accuracy, reliability, or predictive performance.

Preferred Qualifications

• PhD in a quantitative field.
• Knowledge of supervised learning, model evaluation, and feature engineering techniques.
• Experience working with large or complex datasets and translating research concepts into implemented solutions.
• Ability to balance research velocity with analytical rigor, model reliability, and responsible decision-making.
• Strong written and verbal communication skills, with the ability to clearly explain technical concepts and collaborate effectively with cross-functional teams and stakeholders from diverse backgrounds.

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

Time zone requirements The team operates on the East/West coast time zones.

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 or Canada (outside of Quebec) but are expected to spend high quality time in-person collaborating via regular onsites and in-person meetings. The onsite cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

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