Data Scientist, Risk

Who We Are

Imprint is building a platform that helps the world’s best brands grow the lifetime value of their customers. We started with co-branded credit cards and rebuilt them to be smarter, more rewarding, and brand-first. We partner with companies like Crate & Barrel, Rakuten, Booking.com, H-E-B, Fetch, and Shell to launch modern credit programs that deepen loyalty, unlock savings, and drive growth. But the card is just the beginning. We combine advanced payments infrastructure, intelligent underwriting, and deep customer data to predict what each customer will do next and act on it, so brands can offer powerful financial products without becoming a bank.

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-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.

Role Summary

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 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 Success Looks Like in the First 90 Days

Shipped a model or policy change to production that measurably improves approval rates without degrading credit quality

Delivered a deep-dive analysis of the approval funnel - identifying the largest opportunities to safely approvals by channel, segment, or score band, with clear connections to acquisition economics (LTV/CAC, contribution profit)

Built or refined a targeting model for at least one acquisition channel (e.g., affiliate, ITA, prescreen) in partnership with Credit Strategy and Marketing

Responsibilities

Own and improve the top-of-funnel credit decisioning pipeline: application scoring, policy rules, decline waterfalls, and approval rate optimization across all acquisition channels

Build and iterate on underwriting and targeting models (credit scoring, segmentation, propensity) that safe approvals and improve channel-level acquisition quality

Develop targeting criteria and risk frameworks for new and emerging acquisition channels - affiliates, invitation-to-apply, direct mail, instant prescreens, paid social - in partnership with Credit Strategy and Marketing

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 metrics and credit performance

Apply statistical inference, causal analysis, and experimentation design to disentangle policy impact from population shifts and channel mix changes

Build channel-level performance models that connect application volume to credit outcomes: approval rates, expected losses, LTV, CAC, LTV-to-CAC ratios, and contribution profit

Develop monitoring systems that detect approval rate anomalies, score drift, and population mix changes - diagnosing root causes and recommending actions

Build segmentation frameworks to identify underserved populations where credit access can be responsibly expanded

Design and build agentic workflows to automate parts of the risk analytics lifecycle: funnel diagnostics, model monitoring, and policy simulation

Qualifications

Required

5–8+ years of experience in data science, risk analytics, or a related quantitative field - ideally at a high-growth startup or fintech company

Degree in a relevant field (statistics, mathematics, engineering, economics, computer science, or similar)

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

Highly analytical mindset with a bias toward action and a relentless focus on getting the numbers right

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

Comfort owning projects end-to-end and collaborating cross-functionally with Policy, Strategy, Product, and Engineering

Full-stack problem-solving orientation - eager to dive into messy data, trace a decline to its root cause, and question assumptions in pursuit of a better answer

Nice to Have

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 on targeting

Familiarity with affiliate platforms, invitation-to-apply programs, instant prescreens, or direct mail targeting

Experience with model governance, regulatory documentation, or fair lending analysis

Background in time series analysis, forecasting, or optimization

Familiarity with dashboarding tools

Perks & Benefits

Competitive compensation and equity packages

Leading configured work computers of your choice

Flexible paid time off

Fully covered, high-quality healthcare, including fully covered dependent coverage

Additional health coverage includes access to One Medical and the option to enroll in an FSA

20 weeks of paid parental leave for the primary caregiver and 8 weeks for all new parents

Access to industry-leading technology across all of our business units, stemming from our philosophy that we should invest in resources for our team that foster innovation, optimization, and productivity

Compensation Range: $170K - $220K