[All remote jobs](https://jobicy.com/jobs.md)[![Imprint logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2024/02/a28c5741-221.jpeg)](https://jobicy.com/company/imprint.md)[Imprint](https://jobicy.com/company/imprint.md)

# Fraud Analytics Manager

AI Summary Imprint is seeking a hands-on Fraud Analytics Manager to own analytics automation, reporting, and tooling across fraud, credit risk, and operations. The individual contributor will build shared backtesting and risk-label frameworks while reducing repetitive analytical work through Python, SQL, BI, and LLM-enabled solutions. Key early outcomes include automating at least three manual processes, consolidating reporting, and measuring weekly hours saved. The role requires strong technical execution, comfort with complex data, and close partnership with fraud and risk stakeholders. It is a newly created function with significant scope to shape operating practices.[Apply for this job](#job-application)[View company](https://jobicy.com/company/imprint.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 120k–150k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceOpen levelPublished11 Nov 2026Apply before11 Dec 2026Listing views34Application actions2Application toolkit

## Make your next move.

Prepare your resume, explore your fit, and draft a cover letter for this opportunity.

Opportunity details

## About this role.

### Who We Are

[Imprint](https://jobicy.com/company/imprint.md) 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](http://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

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

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

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

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

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

Responsibilities

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

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

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Consolidate fragmented reporting pipelines and deliver clean, reliable reporting infrastructure

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

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Deploy LLM and AI tooling to streamline analytical workflows

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

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

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

Qualifications

Required

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

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Demonstrated analytical ability and comfort diving into messy, complex data

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

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Experience with Git

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Clear, effective communicator who can translate analytical findings into decisions

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

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

Nice to Have

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Experience with LLMs and prompt engineering

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Background in fintech or a high-growth tech startup

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Exposure to fraud, credit risk, or financial domain analytics

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Experience building or scaling analytics functions from scratch

### Perks & Benefits

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Competitive compensation and equity packages

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Leading configured work computers of your choice

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Flexible paid time off

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Fully covered, high-quality healthcare, including fully covered dependent coverage

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Additional health coverage includes access to One Medical and the option to enroll in an FSA

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20 weeks of paid parental leave for the primary caregiver and 8 weeks for all new parents

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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.

Show more

[Apply now >](https://jobicy.com/jobs/161252-fraud-analytics-manager.md)

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![Upload CV](data:image/svg+xml;base64,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)

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## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

4/5EasyHard

### Pace & Pressure

5/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

4/5IndependentCollaborative

AI insightThe role combines fraud-domain analytics with data engineering, backtesting design, reporting consolidation, and AI workflow automation. It requires independently defining an emerging function while delivering practical solutions quickly.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate$135,000US market range (per year)$120k–$165k0$181.50k

AI insightThe employer discloses a base salary range of $120,000 to $150,000 USD per year, with a midpoint of $135,000. This is a credible annual range for a US-based fraud analytics manager with strong SQL, Python, automation, and fintech risk experience. The estimated US market range is $120,000 to $165,000 per year.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Fraud analytics](https://jobicy.com/jobs?search_keywords=Fraud%20analytics.md)[Credit risk analytics](https://jobicy.com/jobs?search_keywords=Credit%20risk%20analytics.md)[Data automation](https://jobicy.com/jobs?search_keywords=Data%20automation.md)[Backtesting](https://jobicy.com/jobs?search_keywords=Backtesting.md)[Business intelligence](https://jobicy.com/jobs?search_keywords=Business%20intelligence.md)[Sigma](https://jobicy.com/jobs?search_keywords=Sigma.md)[Tableau](https://jobicy.com/jobs?search_keywords=Tableau.md)[Git](https://jobicy.com/jobs?search_keywords=Git.md)[LLM tooling](https://jobicy.com/jobs?search_keywords=LLM%20tooling.md)[Prompt engineering](https://jobicy.com/jobs?search_keywords=Prompt%20engineering.md)[Dashboard development](https://jobicy.com/jobs?search_keywords=Dashboard%20development.md)[Reporting infrastructure](https://jobicy.com/jobs?search_keywords=Reporting%20infrastructure.md)[Stakeholder communication](https://jobicy.com/jobs?search_keywords=Stakeholder%20communication.md)

Sample interview questionsHow would you approach building a shared backtesting framework for fraud rules across multiple risk domains?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.

Describe an example of automating a manual analytics process.

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.

How do you work with messy or fragmented data sources?

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.

How would you use LLM tooling responsibly in a fraud analytics environment?

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.

How would you communicate an analytics recommendation to non-technical fraud stakeholders?

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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