🕒 yesterday
Perform laboratory quality assurance analysis and sample data curation, producing data-quality reports and visualizations while developing R-based analytical tools and advising on their operational use.
Senior Analyst (Fraud & Financial Crime)
🕒 yesterday
SQLPythonFraud AnalyticsFinancial Crime Analytics
📜 Description
- Analyse, implement, test and optimise controls that prevent and detect fraud and financial crime.
- Investigate suspicious patterns and emerging threats, then recommend practical responses.
- Monitor detection, losses, false positives and alert volumes to assess control performance.
- Develop and evaluate approaches that improve detection while considering customer and Operations impact.
- Partner with Data Science, Product and Engineering to improve controls using data, models, AI and automation.
- Support model-led controls and control migrations as platforms evolve; help prioritise opportunities and share knowledge.
🛠️ Requirements
- 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.
Full job description
Our Story
Hello there. We’re Zopa.
We started our journey back in 2005, building the first ever peer-to-peer lending company. Fast forward to 2020 and we launched Zopa Bank. A bank that listens to what our customers don’t like about finance and does the opposite. We’re redefining what it feels like to work in finance. Our vision for a new era of banking puts people front and centre — we’ve built a business that empowers everyone to aim high, every day, to move finance forward. Find out more about our fantastic offerings at Zopa.com!
We’re incredibly proud of our achievements and none of it would be possible without the amazing team here. It’s not just industry awards we’re winning, we’ve also been named in the top three UK’s Most Loved Workplaces.
If you embrace unconventional challenges, are unafraid to think differently and are driven to make an outsized impact, you’ll thrive here at Zopa, so join us, and make it count. Want to see us in action? Follow us on Instagram @zopalife
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.
A day in the life
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.
About you
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.
Interview stages
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Recruiter screening — 30 minutes: A conversation about your experience, motivations and interest in the role.
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SQL technical assessment — 1 hour: An opportunity to demonstrate how you write clear, accurate SQL.
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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.
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Final interview — 1 hour, in the office: A conversation about collaboration, decision-making, your experience and Zopa’s values.
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