Lead Fraud Data Analyst – FinTech Card & Lending

Requirements

• A strong data analyst background in a fintech or digital lending environment. You know what SMB or consumer transaction data looks like and you’re comfortable. working with messy, high-volume datasets

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• SQL you can actually use. CTEs, window functions, the difference between WHERE and HAVING. You’ll be tested on this at case study stage

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• A genuine builder instinct. You don’t wait to be told there’s a problem. You find it in the data and you fix it

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• Experience working end-to-end on analytical problems. Not handing logic to a tech team. You own it through to deployment

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• Comfortable in a small, fast-moving team where the answer isn’t always obvious and you have to work it out yourself

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• (Desirable) Direct experience working in card fraud – ideally within a bank, card issuer, or fintech operating on the issuing side

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• (Desirable) Hands-on familiarity with card fraud systems and the data that comes with them (Visa fraud platforms, CNP fraud, 3DS/SCA, PSD2 compliance)

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• (Desirable) Experience with APP fraud or faster payment fraud systems is a strong bonus

What the job involves

• We’re looking for a Senior/Lead Card Fraud Data Analyst/ Analytics Manager to join our Fraud team

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• This is a hands-on, high-ownership role – you’ll take direct responsibility for our fraud systems end-to-end, from onboarding fraud through to card fraud, and play a central part in building the capabilities we need as we launch new products

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• Write complex SQL queries to interrogate transaction and customer data, identify fraud signals, and surface emerging attack patterns

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• Design and build fraud detection rules from scratch, set thresholds based on data, and iterate based on live performance

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• Own rules end-to-end. You spot the pattern, you build the detection, you monitor the outcome

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• Work across card and lending data to understand how our customers behave and where fraud risk sits in our specific product set

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• Contribute to fraud strategy as the team grows, including input into ML-based detection further down the line

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