Senior Product Analyst (AI)

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 The role We’re looking for a Senior Product Analyst to join our AI Tribe. This is a hands-on, builder-style role for someone who thinks in experiments, writes code daily, and is as comfortable building a prototype evaluation framework as they are presenting findings to a product lead.

The role is technical at its core: you’ll be writing SQL and Python to dig into enrichment performance, building evaluation pipelines to test AI-driven approaches, and prototyping experiments that push the accuracy of our transaction intelligence forward. At the same time, this isn’t a data science role — you’ll be expected to prioritise ruthlessly, move fast, and care about outcomes, not just model performance.

The AI tribe The AI Tribe sits at the heart of how Zopa turns data into intelligent products. We build and evolve the capabilities that transform raw transaction data, Open Banking data, and other behavioural signals into reliable, structured intelligence.

A core focus of the tribe is transaction intelligence — how raw transaction data is enriched, categorised, structured, and made usable across the business. This includes merchant and category enrichment, data quality and consistency, and ensuring transaction data 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.