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Greater London, England, United Kingdom
Hybrid
We’re looking for a Senior Analyst to join our Fraud & Financial Crime Analytics team at Zopa.
You’ll use your analytical skills and hands-on SQL experience to help protect our customers and the bank from fraud and financial crime, while making sure genuine customers experience as little friction as possible. Your initial focus will be on card transactions and related events, including card tokens and account takeover.
This is a hands-on role where you’ll own the analysis, implementation and ongoing performance of our controls. You’ll work with large datasets, identify emerging risks and use your findings to design and improve the rules and models that help keep our customers safe.
You don’t need to have worked in fraud or financial crime before. We’re looking for a strong analyst who enjoys solving complex problems, is curious about how things work and wants to build expertise in this area.
Why this role matters
Fraud and financial crime are constantly evolving, and so are the products and services we offer our customers. To grow safely, we need controls that can identify and prevent harm while allowing genuine customers to use their accounts with confidence.
We want to build the next generation of fraud and financial crime controls, using modern data, technology and AI to stay ahead of emerging threats and make our detection as effective as it can be.
You’ll be embedded in one of our product squads as part of the wider Fraud & Financial Crime Analytics team, working alongside Fraud Risk, Operations, Data Science, Engineering and Product. Together, you’ll help shape, build and continuously improve the controls that protect our customers.
This is one of the most impactful analytics roles in the business. In the same day, you could be triaging a new fraud risk, designing the controls to stop it and protecting our customers from harm.
This is an opportunity to bring your own ideas, shape how we solve problems and make a visible impact on both our customers and the business.
Own and improve controls: Analyse, implement, test and optimise rules and other controls that prevent and detect fraud and financial crime.
Investigate emerging risks: Use data to identify suspicious patterns, understand changing threats and recommend practical ways to respond.
Measure what matters: Monitor detection, losses, false positives and alert volumes to understand control performance and identify opportunities to improve.
Test and learn: Develop hypotheses and evaluate new approaches, balancing improvements in detection with the impact on genuine customers and Operations.
Build the next generation of controls: Work with Data Science, Product and Engineering to explore how new data, models, AI and automation can improve our detection and how we work.
Support change: Help implement and monitor model-led controls, and test and migrate controls as our platforms evolve.
Shape your priorities: Work with the squad to identify and prioritise opportunities, influence decisions and share your knowledge with others.
Strong analytical experience and advanced SQL skills, including the ability to work confidently with large, complex datasets.
Experience using Python for analysis, or the ability and willingness to develop those skills.
A track record of owning complex analytical problems from initial question through to a clear recommendation or implemented change.
The ability to take an open-ended problem, work out where to start and turn it into a clear analytical approach.
Experience using data to assess performance, identify opportunities and make decisions.
An understanding of basic machine learning concepts and performance metrics, such as precision and recall, or exposure to using them to assess controls.
Sound judgement and the ability to balance analytical rigour with the need to make timely decisions.
The ability to tell a clear story with data and use your insights to influence technical and non-technical stakeholders.
A collaborative approach and confidence working with people across different teams and levels of seniority.
Curiosity about new tools and an interest in using AI and automation to improve how you work and the controls you build.
It would be great if you also have:
Experience in fraud, financial crime, risk analytics or another controls-focused environment.
Experience analysing card transactions, payments or customer behaviour.
Stronger experience working with machine learning models, including evaluating, implementing or monitoring their performance.
Experience using AI tools such as Claude to support analysis, coding or automation.
Exposure to decisioning systems, rule-based controls or experimentation.
Experience with data visualisation tools such as Tableau.
Experience in financial services, fintech or another fast-moving, product-led environment.
Recruiter screening — 30 minutes: A conversation about your experience, motivations and interest in the role.
SQL technical assessment — 1 hour: An opportunity to demonstrate how you write clear, accurate SQL.
Analytics case study — 1 hour: A discussion about how you approach an analytical problem from end to end. No prior fraud or financial crime experience is required.
Final interview — 1 hour, in the office: A conversation about collaboration, decision-making, your experience and Zopa’s values.
At Zopa we value flexible ways of working.
We value face-to-face collaboration and a good work-life balance. This hybrid role requires you to come to our London office 2-3 days a week.
You'll also have the option of working from abroad for up to 120 days a year!* But no matter where you are, we’ll make sure you’ve got everything you need to thrive, both in your work and home life, from day one.
*Subject to having the right to work in the country of choice
Diversity Statement
Zopa is proud to offer a workplace free from discrimination. Diversity of experience, perspectives, and backgrounds leads to better products for our customers and a unique company culture for our people. We are made up of nearly 50 nationalities, have a DE&I forum made up of Zopians wanting to make a difference and we are proud of our culture where everyone can bring their full self to work. Our approach to DE&I is reflected in our hiring process so please let us know if you require any reasonable adjustments.
Our approach to AI in interviews
At Zopa, AI isn't something we're testing out — it's part of how we work every day. As a proud partner of Jobs 2030, we're committed to building AI fluency across our workforce, and we expect Zopians to use AI as part of how they do their jobs.
Because of that, we want to be transparent about how we think about AI use during our hiring process.
Behavioural and competency-based interviews: please don't use AI. These conversations are designed to understand you — your experiences, your judgment, and how you've approached real situations. An AI-generated answer can't tell us that. What it can do is get in the way of us finding out whether we're the right fit for each other.
Technical interviews: it depends on the role. Some technical stages actively welcome AI use, others don't. Your Talent Partner will let you know what's expected at each stage. Where AI is part of the assessment, we'll be interested not just in the outcome, but in how you used it – the tools you chose, your reasoning, and the decisions you made along the way.
