Analytics Manager (Card Fraud)

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.
• SQL you can actually use: CTEs, window functions, the difference between WHERE and HAVING. You'll be tested on this at case study stage.
• 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.
• Experience working end-to-end on analytical problems. Not handing logic to a tech team. You own it through to deployment.
• Comfortable in a small, fast-moving team where the answer isn't always obvious and you have to work it out yourself.
• Desirable: Direct experience working in card fraud - ideally within a bank, card issuer, or fintech operating on the issuing side.
• Desirable: Hands-on familiarity with card fraud systems and the data that comes with them (Visa fraud platforms, CNP fraud, 3DS/SCA, PSD2 compliance).
• 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.
• 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.
• Write complex SQL queries to interrogate transaction and customer data, identify fraud signals, and surface emerging attack patterns.
• Design and build fraud detection rules from scratch, set thresholds based on data, and iterate based on live performance.
• Own rules end-to-end. You spot the pattern, you build the detection, you monitor the outcome.
• Work across card and lending data to understand how our customers behave and where fraud risk sits in our specific product set.
• Contribute to fraud strategy as the team grows, including input into ML-based detection further down the line.