Staff Applied Scientist

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

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