Senior Product Analyst (Transaction Enrichment)

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.

About the Transaction Enrichment Team

The team sits at the heart of how Zopa turns data into intelligent products. We build and evolve the capabilities that transform raw transaction data into reliable, structured intelligence - so customers can understand their money better, and the rest of the business can build confidently on top of it. A core focus is transaction enrichment: tagging merchants, assigning categories, ensuring data quality, and making sure everything integrates cleanly into our broader data models and customer-facing products. As we move toward more AI-first ways of working, the tribe is also rethinking how transaction data should be accessed and used — not just by analysts and product teams, but by AI systems and LLM-powered tools. The work is foundational and long-term in nature: improving systems, shaping strategy, and building capabilities that compound in value over time.

A day in the life

• Analyse enrichment data (using Python and SQL) to understand where and why enrichment falls short — quantifying problems and building the evidence base for what's worth fixing.
• Surface opportunities the team wouldn't otherwise see: patterns in the data that point to a product gap, a recurring failure mode that suggests a systemic fix, or a signal that something new is worth building.
• Work out how a proposed solution should actually behave in practice — what inputs it needs, where it will struggle, what trade-offs exist between accuracy, cost, and coverage.
• Build quick test harnesses to put a hypothesis in front of data before any engineering resource is committed.
• Design and run evaluations — including LLM-as-judge approaches — to give the team real signal on whether a change improves things and where it introduces new problems.
• Translate what you find into clear recommendations: what to build, why it matters, and what good looks like — so Product and Engineering can make confident decisions.
• Keep up with developments in AI and data quality tooling — form a view on which new techniques are worth experimenting with in the enrichment context.

About you

• Comfortable writing code (typically Python and SQL) to explore data, clean outputs, call APIs, and prototype ideas.
• Some experience working with LLM APIs: calling them, prompting them, structuring their outputs, and understanding why they sometimes give you nonsense.
• You think in experiments: you form a hypothesis before looking at the data, design something that could prove you wrong, and interpret results with appropriate scepticism.
• Naturally curious about messy, imperfect systems — you want to understand why something breaks, not just that it does, and brainstorm ideas to improve it.
• You keep the product goal in view: you care about what changes for customers and the business.
• Comfortable owning ambiguous problems: you can scope the work, prioritise what to test first, and know when good enough is actually good enough.
• Clear communicator — you can explain what you built, what you found, and what it means to engineers, PMs, and stakeholders who don't care about the implementation details.
• 1–3 years in an analytical or technical role; experience in fintech, banking, or a fast-paced data-led environment is a plus but not required.

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.

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.