Fraud Analytics Manager

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.

As a Fraud Analytics Manager, you will own the analytics function supporting fraud, credit risk, and operations at Imprint. This is a hands-on individual contributor role with managerial scope, focused on building automation, tooling, and reporting infrastructure that reduces manual work and drives faster, smarter decisions across the team.

You will act as the primary analytics owner for a set of problems that sit across fraud, credit, and risk: consolidating fragmented reporting, building shared backtesting frameworks, and deploying AI-assisted tooling to free up the team's time for higher-leverage work.

This role is net new — it is not a backfill. It is being created to solve problems that are not currently owned well enough and to establish a new type of analytics function at Imprint.

• Automated or tooled at least 3 manual reports or tasks, delivering complete working solutions for the team

• Consolidated overlapping Claude/LLM skills into a more streamlined, shared framework

• Built an initial backtesting framework for fraud rules, creating a shared process across ATO, first-party fraud, IDT, and credit risk

• Established hours saved per week as the primary KPI for the role, with dashboards and reporting to track it

• Earned trust as a doer who takes action, moves fast, and builds things that last

• →

  Own end-to-end automation and tooling for manual and redundant analytics tasks across fraud and risk

• →

  Build and maintain a consolidated backtesting framework for fraud rules, shared across ATO, first-party fraud, IDT, and credit risk

• →

  Consolidate fragmented reporting pipelines and deliver clean, reliable reporting infrastructure

• →

  Develop and maintain a consolidated risk label framework so changes in one area account for downstream effects in others

• →

  Deploy LLM and AI tooling to streamline analytical workflows

• →

  Establish and track a KPI (hours saved per week) with supporting dashboards and reporting

• →

  Partner with fraud, credit, and risk stakeholders to identify high-impact automation and tooling opportunities

• →

  Help define and refine the scope of this function as it matures

Required

• Strong proficiency in SQL and Python — these are non-negotiable

• Demonstrated analytical ability and comfort diving into messy, complex data

• Experience with BI tooling (Sigma preferred; Tableau or similar accepted)

• Experience with Git

• Clear, effective communicator who can translate analytical findings into decisions

• Bias toward action — takes initiative, ships solutions, and doesn't wait for perfect conditions

• Roughly 3–6 years of relevant analytics experience, though skill set matters more than tenure

Nice to Have

• Experience with LLMs and prompt engineering

• Background in fintech or a high-growth tech startup

• Exposure to fraud, credit risk, or financial domain analytics

• Experience building or scaling analytics functions from scratch