Senior Product Analyst (Transaction Enrichment)

Zopa Bank; Greater London; Hybrid; GBP 70,000 - 95,000; Full time

Job summary

Zopa Bank in London is seeking a Senior Product Analyst for the Transaction Enrichment team. This data-and-product role focuses on understanding enrichment gaps, testing hypotheses, and turning insights into roadmaps with PMs and engineers.

You’ll write Python and SQL to explore data, prototype ideas, and evaluate AI-driven approaches, including prompts and LLM integration. The role combines analytical rigor with pragmatic product thinking in a fast-moving fintech environment.

Qualifications

• Comfortable writing code (Python/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: form a hypothesis before looking at data, design something to prove you wrong, and interpret results with scepticism.
• Naturally curious about messy systems — you want to understand why something breaks and brainstorm improvements.
• You keep the product goal in view: you care about what changes for customers and the business.
• Comfortable owning ambiguous problems: scope the work, prioritise what to test first, and know when good enough is enough.
• Clear communicator — explain what you built, what you found, and what it means to engineers, PMs, and stakeholders who don't care about implementation details.
• 1–3 years in an analytical or technical role; fintech/banking/data-led environment is a plus.

Responsibilities

• Analyse enrichment data (using Python and SQL) to understand where enrichment falls short and build evidence for fixes.
• Surface opportunities: patterns in data indicating product gaps, recurring failures, or new features.
• Define how a proposed solution should behave: inputs needed, limitations, trade-offs between accuracy, cost and coverage.
• Build quick test harnesses to test hypotheses with data before committing engineering resources.
• Design and run evaluations — including LLM-as-judge approaches — to measure impact and detect new problems.
• Translate findings into clear recommendations: what to build, why it matters, what success looks like for Product and Engineering.
• Stay updated on AI and data quality tooling and assess which techniques are worth trying.

Skills

Python

SQL

LLM APIs

Experimentation

Product thinking

Job description

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 !

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. Want to see us in action? Follow us on Instagram @zopalife. 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

At Zopa we value flexible ways of working.

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.