Applied Scientist

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 1,800 signals, powering smarter, fairer decisions for millions of customers.

About the role

The 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.

Responsibilities

• 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.

Requirements

• Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.
• 0–2 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.

Location

This role is available remotely anywhere in the U.S. or Canada (outside of Quebec). As a digital-first company, most work can be accomplished remotely, but employees are expected to participate in regular onsites and in-person meetings. The onsite cadence varies by team, typically once or twice per quarter for 2–4 consecutive days.

Pay

Anticipated base salary range: $141,500—$196,000 USD (varies by geographic location). Total compensation includes base pay, bonus opportunities, and annual equity grants that vest quarterly.

Benefits

• Comprehensive medical, dental, and vision coverage with Health Savings Account contributions from Upstart.
• 401(k) with 100% company match up to $4,500 and immediate vesting, plus after-tax savings options.
• Employee Stock Purchase Plan (ESPP).
• Life and disability insurance.
• Generous holiday, vacation, sick, and safety leave.
• Supportive parental, family care, and military leave programs.
• Annual wellness, technology, and ergonomic reimbursement programs.
• Social activities including team events, all-company updates, employee resource groups (ERGs), and interest groups (e.g., book clubs, fitness, investing, volunteering).
• Catered lunches and snacks when working in offices (Bay Area, Austin, Columbus, or New York City).