Full-time, in-office — Old Street, London.
£60–100k + meaningful equity · 1–5 years experience
The company
Arctal builds structured datasets from unstructured financial documents—100,000+ PDFs (fund reports, regulatory filings, investor letters) turned into clean, queryable data that institutional buyers use for decision‑making.
AI agents do the reading. We build the agents. Team of 5, output of 50.
A dataset is not a fact. It’s a representation of reality that someone chose to stand behind. AI agents do the extraction and structuring—they cannot be the ones standing behind it. That’s your job.
Our customers are asset managers, banks, and financial data firms who need reliable data extracted from documents that were never meant to be machine‑readable.
The role
You’ll be a data-obsessed super‑IC managing a fleet of AI agents.
This role is for someone who has gone deep into data—who knows what great data looks like, who can spot when something is off, and who cares about the difference between “good enough” and “actually correct.” You’ll direct AI agents to do the extraction, but you’re the one who knows whether the output is right.
Data Quality Ownership. You’re the last line of defense before data goes to clients. You know what clean data looks like. You catch the edge cases that agents miss. You build validation logic that encodes your judgement. If the data is wrong, you feel it personally.Agent‑Led Data Pipelines. Build and run data pipelines using AI agents (Claude Code, Cursor, agentic workflows). You’re not doing manual extraction—you’re designing systems that extract reliably at scale. Prompt chains, validation steps, human‑in‑the‑loop checkpoints. When something breaks, you debug it. When something’s slow, you fix it.Data Engineering. Build and maintain pipelines (ingestion, transformation, validation). You’re comfortable in Python, SQL, and the terminal. You’ve wrangled messy data before and you know how to make it clean.
This sits between data science and engineering. You’re not writing production infrastructure, but you’re not doing manual analysis either. You figure out how to get agents to do the work reliably, at scale. Engineers will help you.
What scales is range of judgement. The scarce resource isn’t the person who can do one thing reliably—it’s the person who can hold the whole picture.
You
Data‑first. You’ve spent real time in data—cleaning it, validating it, understanding why it’s wrong. You know the difference between data that looks right and data that is right. You’re the person who notices when the numbers don’t add up.Technically deep. 1–5 years as a data scientist, analytics engineer, or quantitative analyst. You’re fluent in Python and SQL. You’ve built pipelines, not just queried tables. You’ve debugged data issues that took days to find.AI‑native (for real). You actively use AI coding tools—Cursor, Claude Code, or similar—for real work, not just experiments. You know the difference between chat‑based prompting and agentic workflows. You’ve built things with agents, not just talked to them.What we’re filtering for. We need someone who is technical and data‑obsessed. Previous hires that didn’t work out were people who weren’t deep enough in data or weren’t fluent with AI coding tools. If you haven’t spent significant time in the terminal, in codebases, and in messy datasets, this isn’t the right role.
At Arctal, every person is building themselves out of their current role—automating the task they did yesterday so they can take on the harder problem tomorrow.
What this isn’t
- A role where you wait for instructions (you own the delivery function)
- A role with narrow scope (you’ll touch everything from agent prompts to client calls)
- A 9‑to‑5 (intensity is high, learning is faster)
Founders
Aleksi (CEO) — Cambridge engineering + ML. Co‑founded Secondmind, founding team at Sylvera. Previously worked on ML with Carl Rasmussen at Cambridge.
Krista (CCO) — Former Head of Market Intelligence at Climate Bonds Initiative. Deep sustainable finance and capital markets expertise.
Small team that ships fast. No layers, no politics—just building.
What you get
- Old Street office.
- Direct exposure to customers and the full delivery cycle from week one.
- A team that ships fast and doesn’t do meetings for the sake of meetings.
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