Research Scientist

The Team:

At Upstart, our mission is to expand access to credit using advanced machine learning models that better evaluate true risk. As part of this, our teams focus on developing models across various verticals, including Growth marketing, Auto Loans, and Personal Loans Underwriting. These teams are critical to the success of Upstart, directly impacting our customer acquisition efforts and revenue generation through predictive modeling, innovative algorithms, and cross-functional collaboration.

Our Machine Learning (ML) Teams work on creating and deploying production models that power key aspects of Upstart’s business:

• Growth: Models that predict conversion probabilities and prioritize high-potential prospects for outbound marketing.
• Auto Loans: Models that underwrite all auto loans, assessing true borrower risk based on innovative features and methodologies.
• Personal Loan Underwriting: Models that evaluate every applicant’s risk, having a direct impact on Upstart’s core business.

As a Research Scientist, you will have the opportunity to work in one of these highly specialized teams, contributing to the development and deployment of business-critical models, optimizing marketing strategies, underwriting processes, and enhancing business metrics.

The Role:

• Research and develop new machine learning models to enhance the accuracy of predictions in borrower risk, marketing conversions, or underwriting processes.
• Design, prototype, and deploy models to production, ensuring robust and scalable implementations.
• Collaborate cross-functionally with engineers, product managers, and marketing specialists to experiment with and optimize models for business outcomes.
• Evaluate the performance of models, interpret their impact on business metrics, and present actionable insights to stakeholders.
• Continuously explore novel algorithms and methodologies to keep Upstart’s ML systems at the cutting edge of technology.
• Mentor junior team members and contribute to the development of a high-quality, production-ready codebase.

Minimum Qualifications:

• Bachelor’s degree in a scientific or quantitative discipline (e.g., Math, Physics, Computer Science, Statistics, Economics, Operations Research).
• 2+ years of experience in Machine Learning or a similar field.
• Proficiency in Python or other programming languages.
• Strong understanding of statistical, probability, and machine learning theory.
• Full-stack expertise in the modeling process from ideation to production, OR deep expertise in either statistical modeling or machine learning.
• Ability to break down complex tasks into smaller steps and make the right tradeoffs to deliver projects on time.
• Ability to communicate technical concepts effectively to non-technical stakeholders.
• Strong sense of intellectual curiosity, humility, and teamwork.

Preferred Qualifications:

• Master’s degree or PhD in a quantitative discipline (e.g., Math, Physics, Economics, Computer Science, Statistics, Operations Research).
• Deep expertise in Python and experience with machine learning frameworks.
• Experience leading or mentoring junior members in a technical team.
• Proficiency in building and deploying machine learning pipelines and engineering architecture.
• Strong critical thinking skills and ability to develop testable hypotheses.
• Proven ability to meet high standards in terms of quality and velocity with minimal direction.

What you’ll love: 

• Competitive Compensation (base + bonus & equity)
• Comprehensive medical, dental, and vision coverage
• Personal Development and Technology & Ergonomic Budgets 
• Life insurance and disability benefits 
• Clubs and Activities  (Game Nights, Fitstarters, Superwomen, Book Club, Investing Club, Money Discussions, Photography Club and Basketball teams) 
• Generous vacation policy
• 401(k) and Employee Stock Purchase Plan (ESPP)
• Catered lunches + snacks & drinks

Position Location: This role is available in remote locations across the United States. Team collaboration happens through a mix of remote and in-person sessions, with regular on-site meetings happening 1-2 times per quarter.

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