Data scientist jobs in New York, NY

About the role

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, NerdWallet), 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.

What you'll bring

• 5 to 8+ years of experience in data science, risk analytics, or a related quantitative field
• 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 design, and causal analysis, with the ability to disentangle policy impact from population shifts and channel mix changes
• Comfort with AI tools and AI-native workflows
• you actively use tools like Claude, Copilot, or similar to accelerate your work and are excited to build AI-powered analytical systems
• Full-stack problem-solving orientation: you dive into messy data, trace a decline to its root cause, and question assumptions in pursuit of a better answer
• Ability to present complex findings clearly to technical and non-technical audiences, including senior leadership and external partner stakeholders
• Comfort owning projects end-to-end in a fast-moving startup environment with limited scaffolding, collaborating cross-functionally with Policy, Strategy, Product, and Engineering
• Stack
• Experience with credit card underwriting, lending, or consumer credit products
• Familiarity with credit bureau data (Vantage, FICO, tradeline attributes) and alternative data sources

Benefits

Locations