Staff Applied Scientist, Upstart
$199.5-276k
More information about location
AI lending marketplace
Be an early applicant
AI lending marketplace
1001+ employees
Be an early applicant
$199.5-276k
More information about location
1001+ employees
Company mission
To enable effortless credit based on true risk.
Role
Who you are
- 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
What the job involves
- 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
Our take
Based in San Mateo, California, Upstart is piositioning itself as a leading AI lending marketplace, connecting millions of consumers with more than 100 banks and credit unions. In a market where credit access can be limited by traditional scoring methods, Upstart aims to make lending fairer and more accessible by using technology to better understand each borrowers true potential.
Upstart builds AI and cloud-based tools that help lenders approve more borrowers at lower rates. Its models analyze thousands of variables (not just credit scores) to predict creditworthiness more accurately. And through its platform, consumers can access personal, auto, and home-equity loans, as well as smaller "relief" loans, all through a seamless digital experience.
So far, its success is reflected in its high loan automation rate (over 90% of loans are fully processed without human intervention) and the billions in loan originations it has facilitated. The company continues to refine its AI models and expand its offerings, and in 2026 earned provisional approval to establish "Upstart Bank" as a bank in the US.
Freddie
Company Specialist at Welcome to the Jungle
Insights
-16% employee growth in 12 months
Company
Funding (last 2 of 6 rounds)
Apr 2019
$50m
SERIES D
Mar 2017
$32.5m
SERIES D
Total funding: $135.7m
Company benefits
- Comprehensive health plans
- 401k Plan
- Annual wellness reimbursement
- Disability insurance
- Generous vacation policy
- Flexible time off
- Parental leave
- Family forming benefits through Carrot
- Technology equipment budget
- Personal development budget
- Various clubs and extracurricular activities
- Catered lunches
- Fully stocked micro-kitchen
Company HQ
South San Mateo, San Mateo, CA
Leadership
David Girouard
(Founder & Executive Chair)They started their career at Accenture and Booz Allen & Hamilton before working in Product Management at Apple and Virage. They left their role of 8 years as President of Enterprise at Google to found Upstart in March 2012.
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