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- United Kingdom
- Work type
- Hybrid
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The role
Every transaction made by a Zopa customer passes through our transaction enrichment pipeline. We tag the merchant and assign a spending category, so customers can understand their money better and the rest of the bank can build on reliable data. This role is about making that classification measurably better.
As an Applied AI Analyst, you will design and tune the prompts that do the categorising, work out where and why they fall short, and help shape what we build next. At heart this is an analytical role, but one where product thinking matters as much as technical ability. You will move quickly, make pragmatic trade-offs, and care about what ships and what lands.
You will work closely with the Product and Engineering team to make sure your findings influence the roadmap. You will be comfortable using LLM APIs directly and building with AI coding tools, and you will need the underlying understanding to use them well.
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
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Design, test and refine the prompts that tag merchants and assign spending categories, and work out how transaction data should be prepared and passed to the model.
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Analyse enrichment results using Python and SQL to understand where and why enrichment falls short and making evidenced recommendations for improvements.
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Surface opportunities the team would not otherwise: spot patterns in the data that point to a product gap, a recurring failure mode that suggests a systemic fix.
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Design and run evaluations, including LLM-as-judge approaches, to analyse whether a change improves things or introduces new problems.
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Build quick, scrappy prototypes and test harnesses to test a hypothesis with real data before any engineering resource is committed.
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Shape the future of the product, for example how customers could re-categorise transactions, and how enriched data can support a wider range of Zopa products and AI-powered tools.
About you
We hire for capability rather than years, though this is typically 1-3+ years in an analytical or technical role. You’ll likely bring many of the following:
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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.
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You are curious about messy, imperfect systems: you want to understand why something breaks, and you brainstorm ideas to improve it.
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You can describe what you want and build it together with an LLM, to explore data, clean outputs, call APIs and prototype ideas.
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You have some experience with LLM APIs: calling them, prompting them, structuring their outputs, and understanding why they sometimes give you nonsense.
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You keep the product goal in view: you care about what changes for customers and the business.
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You are comfortable owning ambiguous problems: you can scope the work, prioritise what to test first, and know when good enough is good enough.
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You are a clear communicator who 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.
Nice to have
None of this is required, and we welcome smart people who have not done it yet. If you have experience of any of the following, we would love to hear about it:
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Evaluating LLM outputs, including LLM-as-judge approaches, ground truth sets or evals.
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Prompt engineering and prompt tuning, and comparing LLMs on accuracy, cost and coverage.
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Classification, categorisation, labelling or entity-matching problems.
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Banking, fintech, payments or another fast-paced, data-led environment.
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Side projects or things you have built with LLMs simply because you wanted to.
What this role is not:
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This is not a research role. We value what ships and what lands over what looks good in a notebook.
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This is not a production engineering role. Engineering owns production code and deployment. You may occasionally contribute where it makes sense, but that is not your primary responsibility.
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This is not a reporting role. Your value lies in the recommendations you make and what changes as a result.
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.
