Staff Applied Scientist

Upstart

3d•$199,500 - $276,000•Remote

About The Position

Upstart’s Unsecured Underwriting Machine Learning team develops and improves models that inform credit decisions for unsecured lending products. The team conducts machine learning research, evaluates model performance, and partners with Engineering to translate promising ideas into scalable model improvements. The Staff Applied Scientist will research and implement enhancements to Upstart’s core unsecured underwriting model, directly influencing credit decisioning while helping increase research velocity and maintaining the rigor, reliability, and responsible oversight required for production underwriting models. The position is remote and the team operates across East and West Coast time zones. Most work can be performed remotely, but employees are expected to participate in regular in-person onsites and meetings. Most teams meet once or twice per quarter for two to four consecutive days. The role is available in the United States and Canada outside Quebec. The anticipated U.S. remote base salary range is $199,500–$276,000. Compensation varies by geographic region and is also determined by job-related skills, experience, and education or training. The total compensation package may include target bonuses, equity compensation, and benefits.

Requirements

Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.
At least 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.

Nice To Haves

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, including the ability to explain technical concepts clearly and collaborate effectively with cross-functional teams and stakeholders from diverse backgrounds.

Responsibilities

Research machine learning and statistical approaches to 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 proposed changes improve decisioning reliably.
Partner with Engineering teams on technical reviews, implementation, and deployment.
Translate research findings into clear recommendations, documented methodologies, and production-ready solutions.

Benefits

Target bonuses and equity compensation.
401(k) or Group Retirement Savings Plan with a company match of $2 for every $1 contributed, up to $15,000 annually.
Employee Stock Purchase Plan with discounted stock purchase options for eligible U.S. employees.
Medical, dental, and vision coverage, wellness resources, and supplemental health coverage in Canada.
Health Savings Account contributions for eligible U.S. plans.
Life insurance and disability coverage.
Paid time off, sick leave, and company holidays in line with local requirements.
Paid family and parental leave.
Family-centered fertility, parenthood, and caregiving benefits.
Employee Assistance Program, including mental health support and life-centered resources.
Financial wellness resources, including financial planning tools and a financial concierge service for U.S. employees.
Annual wellness allowance.
Annual productivity allowance for relevant work tools and resources.
Team events, all-company updates, and employee resource groups.
Catered lunches and stocked micro-kitchens at the Bay Area, Austin, Columbus, and New York City offices.