Data Scientist, Risk

Overview

Imprint is building a platform that helps the world’s best brands grow the lifetime value of their customers. We partner with companies like Crate & Barrel, Rakuten, , H-E-B, Fetch, and Shell to launch modern credit programs that deepen loyalty, unlock savings, and drive growth. Backed by Kleiner Perkins, Thrive Capital, Ribbit, and Khosla Ventures, we’re building a world-class team to redefine how people pay and how brands grow.

If you want to move fast, solve hard problems, and own real outcomes, we want to meet you.

The Risk team at Imprint is responsible for making smarter, faster credit decisions that balance growth with responsible risk management. The team builds the models, policies, and analytical systems that power underwriting, fraud detection, and portfolio optimization across all of Imprint’s credit programs.

As a Data Scientist, Risk, you will own the modeling powering Imprint’s top-of-funnel credit decisioning—from application intake through approval—across every acquisition channel: direct affiliates (Credit Karma, Nerd Wallet), invitation-to-apply emails, direct mail, paid social, instant prescreens, and on-site applications. Your primary focus will be improving approval rates while maintaining credit quality: building better underwriting models, designing policy experiments, and uncovering segments where we can safely expand access to credit.

This role sits at the intersection of credit and acquisition strategy. You will partner directly with Credit Strategy, Product, Engineering, and Marketing to build targeting models for new channels, evaluate channel-level credit performance, and connect acquisition volume to downstream economics—approval rates, vintage loss forecasts, LTV, CAC, and contribution profit. Increasingly, that means building not just analyses but AI-powered systems that can autonomously monitor approval rate, channel performance, diagnose shifts, and recommend policy adjustments.

• Own and improve the full top-of-funnel credit decisioning pipeline: application scoring, policy rules, decline waterfalls, and approval rate optimization across direct affiliates, invitation-to-apply, direct mail, paid social, instant prescreens, and on-site applications
• Build and iterate on underwriting, targeting, and segmentation models that expand safe approvals and improve channel-level acquisition quality
• Design and analyze A/B tests and champion/challenger experiments on credit policies, establishing a test-and-learn cadence with structured readouts on both acquisition and credit performance
• Build channel-level performance models that connect application volume to downstream economics: approval rates, expected losses, LTV, CAC, and contribution profit
• Design and build agentic workflows and AI-powered monitoring systems that autonomously detect approval rate anomalies, diagnose score drift and population mix changes, and recommend policy adjustments
• Partner directly with Credit Strategy, Product, Engineering, and Marketing to develop targeting criteria and risk frameworks for new and emerging acquisition channels
• Build segmentation frameworks to identify underserved populations where credit access can be responsibly expanded

Required

• 5 to 8+ years of experience in data science, risk analytics, or a related quantitative field, ideally at a high-growth startup or fintech company
• Strong Python and SQL skills, with the ability to build models, transform raw data, and create custom datasets from complex financial data
• Experience building credit risk or targeting models (scorecards, underwriting models, segmentation) or similar predictive modeling in a regulated environment
• Deep understanding of statistical inference, experimentation…