Applied Scientist

About the Team

Upstart’s Partnerships team helps drive customer acquisition and originations through partner offers. The team is focused on improving how offers are optimized across the customer journey.

As the Applied Scientist on the Partnerships team, you’ll use causal inference, experimentation, and machine learning to help determine which offers are most likely to support customer conversion. You’ll take on a greenfield optimization opportunity, moving from research and measurement to production-ready models and decision strategies.

How You’ll Make an Impact

• Develop causal machine-learning models to optimize customer offers at different points in the funnel, from marketing-time decisions with limited information to decisions informed by customer activity on our site.
• Analyze and interpret complex data, identify gaps in what we know, and design experiments to gather evidence for building and evaluating optimization models.
• Measure the impact of offer changes with statistically rigorous experiments and evaluations.
• Translate research and results into clear recommendations, decision strategies, and next steps for business and technical partners.
• Build production-ready models and code for use in offer selection.
• Partner with Partnerships leadership, Product, Engineering, peer machine-learning teams, and model-risk partners to align on strategy, implementation, and methodology.

Minimum Qualifications

• 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 applying causal inference methods and experimentation to real-world decisions in an applied-science or data-science context.
• Applied statistics, probability, and experimental-design expertise used to build or evaluate models or interventions.
• Production-quality coding experience in Python, including data manipulation and algorithm implementation.

Preferred Qualifications

• PhD in a quantitative field.
• Experience with marketplace optimization, particularly where pricing or offer decisions are central.
• Experience with causal machine-learning methods or treatment-effect modeling.
• Experience with supervised learning, model evaluation, and feature engineering, especially with large or complex datasets.
• Experience taking work from exploratory analysis and experimentation through production-model development while maintaining analytical rigor and model reliability.
• Ability to translate broad business problems into structured research questions and communicate complex findings as actionable recommendations.

Travel Requirements

As a digital-first company, the majority of work is remote. Most teams meet in-person once or twice per quarter for 2-4 consecutive days at a time.