Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Fraud Analytics Manager
- Remote from
- USA
- Salary
- USD 120k–150k / yr
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Open level
- Published
- Apply before
- 11 Dec 2026
- Listing views
- 16
- Application actions
- 1
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About this role.
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.
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.
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
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
Imprint is committed to a diverse and inclusive workplace. Imprint is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. Imprint welcomes talented individuals from all backgrounds who want to build the future of payments and rewards. If you are passionate about FinTech and eager to grow, let’s move the world forward, together.
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Role DNA
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Core skills
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Sample interview questions
I would first align stakeholders on common outcome labels, decision windows, and success metrics such as fraud loss, approval impact, and false-positive rates. I would then build versioned datasets and reusable SQL or Python evaluation modules so each rule can be tested consistently, with dashboards documenting results and trade-offs.
I would identify the most repetitive steps, map data dependencies, and build a reliable pipeline with validation checks and clear ownership. I would measure the time saved, monitor data quality, and document the workflow so the team can maintain and extend it.
I start by profiling completeness, definitions, granularity, and refresh patterns. I establish a canonical data model and reconciliation checks, then prioritize fixes that improve decision quality and reduce recurring operational effort.
I would use LLMs for controlled tasks such as documentation, query assistance, workflow triage, and summarization while keeping sensitive data governed and access restricted. Any production output would have validation, auditability, human review where appropriate, and measurable quality criteria.
I would lead with the decision, expected business impact, and key trade-offs rather than technical implementation details. I would use a concise dashboard or narrative supported by clear assumptions, confidence levels, and recommended next actions.
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