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
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Arct a L!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Scientist, AI Agents at Arct a L.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Arct a L.
✨Apply Directly through Our Website
When you find a suitable opening like Data Scientist, AI Agents at Arct a L, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Scientist, AI Agents in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Arct a L, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Arct a L. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Arct a L
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Arct a L!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
