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Greater London, England, United Kingdom
Hybrid
We’re looking for a Senior Product Analyst to join our Transaction Enrichment team. At heart this is a data and analytical role — but one where strong product thinking matters as much as technical ability. You'll be expected to care not just about what the data shows, but about what should change as a result.
You'll spend your time understanding where our AI-driven transaction enrichment falls short, forming hypotheses, building prototypes to test them, and shaping what we build next. This isn't a research role - you'll be expected to move quickly, make pragmatic trade-offs, and care about what ships and what lands, not just what looks good in a notebook.
You'll be comfortable writing code and using LLM APIs directly. You won't be starting from scratch every time - AI coding tools are part of how we work, but you'll need the underlying capability to use them well.
Your output is insight and evaluation — shaping what we build and the implementation decisions behind it: which prompts work, how data should be processed, where the trade-offs lie. You'll work closely with the Product Manager and Engineering to ensure your findings land in the roadmap and translate into real changes.
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.
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
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.
*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.