Data Scientist, Risk

Co-branded cards alone account for over $300 billion in U.S. annual spend, and most still run on legacy bank rails. Imprint is the modern alternative: flexible, embeddable, and built for how people actually pay today. Backed by Kleiner Perkins, Thrive Capital, Ribbit, and Khosla Ventures, we’re building a world-clas...

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 appli...

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 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
Experience with credit card underwriting, lending, or consumer credit products
Familiarity with credit bureau data (Vantage, FICO, tradeline attributes) and alternative data sources
Experience building or scaling experimentation infrastructure for credit policy testing
Exposure to fraud detection, KYC/IDV workflows, or application fraud models
Understanding of acquisition channel economics and experience partnering with marketing or credit strategy teams on targeting and LTV modeling
We don't expect every candidate to check every box. If this role excites you and you bring strong fundamentals, we encourage you to apply.
Python and SQL for modeling and analysis. Snowflake for data warehousing. AWS infrastructure. Dashboarding and monitoring tools for production systems.
Learn more about how we build at Imprint on our engineering blog: https://medium.com/imprint-eng