AnthropicRemote · San Francisco, CA | New York City, NY | Seattle, WA$270k-$310k
ZopaPosted today
Senior Product Analyst (Transaction Enrichment)
Senior Product Analyst (Transaction Enrichment) at Zopa scores 65 out of 100 on AI centrality, which makes it a Level 3 role on this board.
AI in this role
Senior product analyst focused on evaluating, prototyping, and improving AI-driven transaction enrichment systems.
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.
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