At a Glance
- Tasks: Manage AI agents to extract and structure data from financial documents.
- Company: Join Arctal, a dynamic tech company revolutionising financial data processing.
- Benefits: Competitive salary, equity options, and a fast-paced learning environment.
- Other info: Fast-paced team with direct customer exposure and no unnecessary meetings.
- Why this job: Make a real impact in data quality and work with cutting-edge AI technology.
- Qualifications: 1-5 years experience in data science, strong Python and SQL skills required.
The predicted salary is between 60000 - 100000 £ per year.
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.
Data Scientist, AI Agents employer: Arct a L
At Arctal, we pride ourselves on being an exceptional employer that fosters a dynamic and innovative work culture in the heart of London. Our team thrives on collaboration and agility, allowing you to take ownership of your projects while directly impacting our clients from day one. With a focus on personal growth and the opportunity to work with cutting-edge AI technologies, we empower our employees to continuously develop their skills and advance their careers in a fast-paced environment.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist, AI Agents
✨Tip Number 1
Get your networking game on! Connect with folks in the data science and AI space, especially those who work at companies like Arctal. Attend meetups, webinars, or even just reach out on LinkedIn. You never know who might have a lead on a job or can give you insider info!
✨Tip Number 2
Show off your skills! Create a portfolio that highlights your data projects, especially those involving AI agents or data pipelines. Use GitHub to showcase your code and document your thought process. This will help us see your technical depth and how you tackle real-world problems.
✨Tip Number 3
Prepare for interviews by brushing up on your problem-solving skills. Be ready to discuss how you've handled messy datasets or built validation logic in the past. We want to see your judgement in action, so think of examples where you caught errors or improved processes.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at Arctal. Don’t forget to tailor your application to highlight your data obsession and AI experience!
We think you need these skills to ace Data Scientist, AI Agents
Some tips for your application 🫡
Show Your Data Passion:We want to see your love for data shine through in your application. Share specific examples of how you've worked with data, cleaned it, and validated it. Make it clear that you know the difference between good data and great data!
Be Technical and Specific:When detailing your experience, be sure to mention the tools and languages you're fluent in, like Python and SQL. We’re looking for someone who’s not just dabbled but has really rolled up their sleeves and built data pipelines or debugged tricky issues.
Highlight Your AI Experience:Since we’re all about AI agents, let us know how you’ve used AI coding tools in your work. Whether it’s building workflows or using agentic systems, show us that you’re not just familiar with AI but that you actively use it to solve real problems.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to see your application in the right context. Plus, it shows you’re keen on joining our team at Arctal!
How to prepare for a job interview at Arct a L
✨Know Your Data Inside Out
Before the interview, dive deep into your past experiences with data. Be ready to discuss specific examples where you cleaned, validated, or transformed datasets. Highlight your understanding of what makes data 'good' versus 'bad'—this will show that you’re not just technically skilled but also data-obsessed.
✨Familiarise Yourself with AI Tools
Make sure you’re comfortable discussing AI coding tools like Cursor and Claude Code. Prepare to explain how you've used these tools in real projects, not just in theory. This will demonstrate your hands-on experience and your ability to leverage AI for data extraction and structuring.
✨Prepare for Technical Questions
Expect technical questions related to Python, SQL, and data pipelines. Brush up on your coding skills and be ready to solve problems on the spot. Practising common data engineering scenarios can help you articulate your thought process clearly during the interview.
✨Show Your Problem-Solving Skills
Be prepared to discuss times when you encountered data issues and how you debugged them. Share specific examples of edge cases you caught or validation logic you built. This will illustrate your critical thinking and your commitment to delivering clean, reliable data.
