Data Scientist (Risk)

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

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

Stack:

Python and SQL for modeling and analysis. Snowflake for data warehousing. AWS infrastructure. Dashboarding and monitoring tools for production systems

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

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

Comfort owning projects end-to-end in a fast-moving startup environment with limited scaffolding, collaborating cross-functionally with Policy, Strategy, Product, and Engineering

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

Ability to present complex findings clearly to technical and non-technical audiences, including senior leadership and external partner stakeholders

Experience building credit risk or targeting models (scorecards, underwriting models, segmentation) or similar predictive modeling in a regulated environment

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

Strong Python and SQL skills, with the ability to build models, transform raw data, and create custom datasets from complex financial data

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