Senior Product Analyst (Transaction Enrichment)
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.
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