AI Analyst; Mid and Senior Level; Transaction Enrichment

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.

• 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.
• Analyse enrichment results using Python and SQL to understand where and why enrichment falls short and making evidenced recommendations for improvements.
• 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.
• Design and run evaluations, including LLM-as-judge approaches, to analyse whether a change improves things or introduces new problems.
• Build quick, scrappy prototypes and test harnesses to test a hypothesis with real data before any engineering resource is committed.
• 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.
  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:
• 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.
• You are curious about messy, imperfect systems: you want to understand why something breaks, and you brainstorm ideas to improve it.
• You can describe what you want and build it together with an LLM, to explore data, clean outputs, call APIs and prototype ideas.
• You have some experience with LLM APIs: calling them, prompting them, structuring their outputs, and understanding why they sometimes give you nonsense.
• You keep the product goal in view: you care about what changes for customers and the business.
• You are comfortable owning ambiguous problems: you can scope the work, prioritise what to test first, and know when good enough is good enough.
• 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.
  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…