ВеликобританияSenior Product Analyst (Transaction Enrichment)
3 нед. назад
ВеликобританияSeniorHybrid
data analysisaitransaction enrichment
Senior Product Analyst role focused on transaction enrichment combining data analysis with product thinking to drive meaningful changes.
О компании
- Our Story Hello there. We’re . We started our journey back in 2005, building the first ever peer-to-peer lending company. Fast forward to 2020 and we launched 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 .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 , 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 r
Требования
- 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
Другое
- 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