Principal Credit Analytics

The Team

Upstart’s Credit Analytics team is at the center of how we understand, forecast, and communicate credit performance across the company. The team builds the frameworks, infrastructure, and insights that connect our AI-driven underwriting, portfolio performance, and funding strategy, ensuring decisions are grounded in a rigorous and forward-looking view of risk and return.

As a Principal Credit Analytics, you will lead a key pillar of these functions, credit forecasting & valuation or risk capital analytics—driving high-impact analytics that inform portfolio strategy, financial planning, and funding decisions. You’ll partner closely with Machine Learning, Finance, Product, and Capital teams to translate complex credit dynamics into actionable insights and scalable systems.

How you’ll make an impact

• Own a core analytics domain within Credit Analytics (forecasting & valuation or risk capital), driving execution and continuous improvement of frameworks that power portfolio monitoring, planning, and decision-making
• Develop and enhance credit forecasting and valuation methodologies, including loss forecasting, scenario analysis, and portfolio performance measurement, ensuring outputs are accurate, explainable, and decision-useful
• Contribute to risk capital analytics and reporting, supporting funding strategies such as securitizations, warehouse facilities, and forward-flow programs through robust performance tracking, stress scenarios, and risk/return analysis
• Translate analytics into business impact, delivering clear insights and recommendations that guide product strategy, credit policy, and funding decisions
• Partner cross-functionally with ML, Finance, Product, Engineering, and Capital teams to align on definitions, metrics, and frameworks, and to integrate analytics into core workflows and decision processes
• Build and scale analytics infrastructure, including data models, pipelines, dashboards, and reporting tools that enable consistent, self-serve insights across stakeholders
• Uplevel analytical rigor and processes, establishing best practices for data quality, model validation, and documentation that scale with the business