Data Scientist, Fraud Risk

Who We Are Imprint 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 create delightful and personalized experiences for members as well as efficient and profitable relationships for our brand partners. Our robust technology and world-class operations allow us and our brand partners to offer powerful financial products without becoming a bank. In the U.S., co-branded cards alone account for over $300 billion in annual spend, and most still run on decades-old legacy bank systems. 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. The Team The Risk team at Imprint builds the models, policies, and analytical systems that protect our credit card programs [... source excerpt omitted ...] t, synthetic identity, first-party fraud, and other forms of application abuse while minimizing false positives, unnecessary verification, and friction for legitimate applicants. You will partner closely with Fraud Strategy and Operations, Product, Engineering, Compliance, and Credit Strategy to improve onboarding fraud and KYC decisioning. You will build models, evaluate third-party fraud and identity vendors, test new scores and attributes, design experiments, and translate emerging fraud patterns into scalable policy changes. You will also build monitoring and AI-powered analytical workflows that detect shifts, diagnose root causes, and help the team respond quickly as fraud [... source excerpt omitted ...] n, data-quality issues, and new attack patterns—and recommend adjustments for human review Partner with Fraud Operations, Product, Engineering, Compliance, and Credit Strategy to productionize changes, validate their impact, and communicate recommendations to senior leadership and external partners Your Profile Required 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 and evaluating predictive models for fraud, identity, KYC