Fraud Analytics Manager

Imprint helps the world's best brands grow the lifetime value of their customers. They specialize in modern credit programs, combining advanced payments infrastructure, intelligent underwriting, and deep customer data to create personalized experiences. Imprint is a modern alternative to legacy bank systems, offering flexible, embeddable solutions for how people pay today.

As a Fraud Analytics Manager, you will own the analytics function supporting fraud, credit risk, and operations. 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 problems spanning fraud, credit, and risk, consolidating fragmented reporting, building shared backtesting frameworks, and deploying AI-assisted tooling.

What Success Looks Like in the First 90 Days:

• 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

Responsibilities:

• 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

Qualifications:

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