At a Glance
- Tasks: Manage AI agents to extract and structure data from financial documents.
- Company: Join Arctal, a fast-paced tech company transforming financial data.
- Benefits: Competitive salary, equity options, and a dynamic work environment.
- Other info: Fast learning environment with direct customer exposure from day one.
- Why this job: Make a real impact by ensuring data quality for major financial clients.
- Qualifications: 1-5 years 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.
- 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 in London 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 Old Street, London. Our team thrives on collaboration and agility, allowing you to take ownership of your projects from day one while enjoying meaningful equity and competitive salaries. With a strong focus on employee growth, we encourage continuous learning and provide opportunities to tackle complex challenges, making every day rewarding and impactful.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist, AI Agents in London
✨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 and debugged issues in the past. We want to hear about your experiences and how you ensure data quality—so come armed with examples!
✨Tip Number 4
Apply through our website! It’s the best way to get noticed. Tailor your application to highlight your experience with Python, SQL, and AI tools. Make sure to express your passion for data and how you can contribute to our mission at Arctal!
We think you need these skills to ace Data Scientist, AI Agents in London
Some tips for your application 🫡
Show Your Data Passion:Make sure to highlight your love for data in your application. We want to see that you’re not just familiar with data, but that you’re truly obsessed with it. Share examples of how you've cleaned, validated, or transformed data in the past.
Be Specific About Your Skills:When listing your skills, be specific about your experience with Python, SQL, and AI coding tools. We’re looking for someone who can demonstrate their technical depth, so don’t hold back on the details!
Tailor Your Application:Don’t send a generic application! Tailor your CV and cover letter to reflect the job description. Mention how your previous experiences align with our needs, especially around data quality ownership and building data pipelines.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people!
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 'just okay'—this will show that you’re not just technically skilled but also data-obsessed.
✨Show Off Your AI Tool Skills
Familiarise yourself with AI coding tools like Cursor and Claude Code. Be prepared to explain how you've used these tools in real projects, not just in theory. Discuss any agentic workflows you've built and how they improved your data processes—this will demonstrate your hands-on experience and technical depth.
✨Prepare for Problem-Solving Questions
Expect questions that test your ability to debug data issues or design data pipelines. Think of scenarios where you faced challenges with data extraction or validation. Articulate your thought process clearly—showing how you approach problems will highlight your critical thinking skills and your ability to own the delivery function.
✨Understand the Company’s Mission
Research Arctal and its focus on turning unstructured financial documents into clean data. Be ready to discuss how your skills align with their mission and how you can contribute to their goals. Showing that you understand their business will set you apart and demonstrate your genuine interest in the role.
