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Imprint is a fintech company that modernizes co-branded credit card programs for major brands like Crate & Barrel, Rakuten, and Booking.com. The company combines payments infrastructure, intelligent underwriting, and customer data to deliver smarter, brand-first credit products without requiring partners to become banks.
The Risk team builds models, policies, and analytical systems that protect credit card programs while maintaining a seamless member experience. As a Data Scientist focused on Onboarding Fraud, you will own the modeling and analytics powering fraud and identity decisions from application submission through account opening.
Your core responsibilities include: owning Imprint's onboarding fraud decisioning across the full application journey (identity verification, KYC controls, application fraud models, policy rules, decline/verification waterfalls, and manual-review strategies); building, validating, deploying, and monitoring models that detect identity theft, synthetic identity, first-party fraud, and coordinated application abuse using identity, device, behavioral, application, bureau, network, and consortium signals; evaluating third-party fraud and identity vendors by testing scores and attributes, measuring incremental lift, overlap, coverage, stability, latency, and cost; designing and analyzing A/B tests, shadow tests, holdouts, and champion/challenger strategies that balance fraud losses against approval rate, false positives, verification friction, and manual-review volume; investigating emerging fraud patterns and decision misses to develop new features, rules, models, and review strategies; building monitoring and AI-powered workflows that detect model drift, population shifts, vendor degradation, data-quality issues, and new attack patterns; and partnering with Fraud Operations, Product, Engineering, Compliance, and Credit Strategy to productionize changes and communicate recommendations to leadership and external partners.
You will need 5–8+ 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 are essential, along with experience building and evaluating predictive models for fraud, identity, KYC, AML, credit risk, or other adversarial classification problems. You should have deep understanding of supervised machine learning, model validation, backtesting, calibration, feature engineering, and production model monitoring. Statistical inference and experiment design expertise (A/B tests, holdouts, champion/challenger tests, causal measurement, tradeoff analysis) is required. You must be able to evaluate decision systems holistically—not just model performance—using metrics such as fraud capture, loss rate, false-positive rate, approval impact, verification friction, operational workload, and economic value. Full-stack problem-solving orientation is critical: tracing decisions through raw inputs, vendor responses, model scores, policy rules, and downstream outcomes to diagnose root causes. You should be comfortable owning projects end-to-end, from problem definition through production implementation, monitoring, and business impact measurement. Strong communication skills for translating complex analytical findings and decision tradeoffs to technical and non-technical audiences are essential. Experience with AI tools to accelerate analysis, investigation, feature development, documentation, and monitoring—and enthusiasm for building AI-powered risk systems—is highly valued. Nice-to-have skills include application or onboarding fraud experience (identity theft, synthetic identity, first-party fraud, application manipulation, fraud rings) and familiarity with KYC, CIP, identity verification, document verification, device intelligence, behavioral signals, consortium data, or credit bureau data.
