Research Scientist

Upstart
United States | RemoteUp to $15kPosted 19 March 2026

Job Description

About Upstart 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. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress. We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City (opening Summer 2026), you’ll have the support to work in the way that works best for you. If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you. The Team: Machine Learning is at the heart of Upstart’s business model: our models are the product. Our team includes research scientists, data scientists, and machine learning engineers who build and improve production models and the systems around them across the funnel: underwriting and pricing, fraud detection, performance marketing, loan servicing and fair lending/explainability. We tackle high‑impact problems, from underwriting and pricing to monitoring and fairness, where creativity, rigor, and strong engineering directly move the business. The Role: As a Research Scientist, you will work on real-world applied machine learning problems. You will contribute to the research, development and deployment of business-critical models across many applications and products. This role covers all subteams at Upstart. How You’ll Make an Impact: Research and develop new machine learning models to enhance the accuracy of predictions across many applications (marketing, pricing, servicing, monitoring fairness) and products (personal/auto/HELOC loans) Design, prototype, and deploy models to production, ensuring robust and scalable implementations. Collaborate cross-functionally with engineers, product managers, and 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, humil ... (truncated, view full listing at source)
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