At a Glance
- Tasks: Lead data processing from raw documents to structured datasets using AI agents.
- Company: Exciting start-up transforming financial data for major institutions.
- Benefits: Up to £100k salary, profit share, equity, and a dynamic work environment.
- Other info: Join a small, innovative team with significant growth potential.
- Why this job: Own your work and make a real impact in a high-visibility role.
- Qualifications: 2-5 years of hands-on data experience and strong Python/SQL skills.
The predicted salary is between 90000 - 100000 £ per year.
Salary: up to ~£100k + profit share
Equity: 0.75 - 1%
Location: Old Street (4-5 office days/week)
About the company
This early stage start-up is processing hundreds of thousands of unstructured financial documents into clean, structured datasets for some of the world's largest financial institutions - producing the output of 50, with a team of 5. Forecasting £1.5m revenue within their first 12 months, they have immense potential - not based on hype or inflated valuations, but rather achieving mega productivity through intelligent application of AI agents. Their mid-term goal is ~£50m revenue with a sub-30 person team.
What's in it for you?
- Profit share.
- Cash in your account on a regular basis - not a promise of a huge payout IF the company succeeds and sells.
About the role
At most data companies, a dataset is the output of a large analyst team. Here, it's the output of a fleet of AI agents - directed by one person who stakes their reputation on it being right. That's this role. You're not downstream. You're not cleaning data someone else built. You start from the source – raw documents, filings, internet data – and you build the dataset. You're the reason institutional clients – banks, hedge funds, investment firms – trust the data. AI agents do the extraction. You direct them, interrogate the output, catch what they miss and encode your judgement into validation systems that make the whole pipeline better over time. You'll also work directly with clients, explaining methodology to sophisticated buyers who need to understand what they're relying on. This isn't QA. It's the highest-ownership, most client-visible position in the company. Your name will be on the data. And for that, you'll have a very senior seat at the table.
Must have requirements
- Roughly 2-5 years working directly with data at a company where data is the core product – not as an analyst consuming clean datasets, but building them from messy, unstructured sources.
- Demonstrable obsession with data quality – you know what great data looks like because you've spent time making it from scratch.
- You go to the row level. A missing data point or unexplained anomaly bothers you until it's resolved – not flagged and forgotten.
- Genuinely AI-native: you've been using agentic tooling long enough to have opinions on it – Claude Code, Cursor, OpenAI Agents SDK or equivalent, used on daily basis.
- Python and SQL fluent – you've built and debugged pipelines, not just queried tables.
- Comfortable in the terminal, in codebases and in real-world messy data environments.
- Experience at a financial data provider (Bloomberg, Refinitiv, Preqin, FactSet etc.) or in quant/ESG research.
- You've built agents yourself – not just used them.
- Experience with LLMs in production / agentic workflow design.
- Web scraping and document parsing at scale.
- Experience in a small team (2-30 people) where you owned the whole function.
VISA sponsorship is available if needed (but you need to be already living in the UK).
Data Lead in London employer: Wave Group
At Wave Talent, we pride ourselves on being an exceptional employer, offering a unique opportunity for the Founding BDR to shape the future of our fast-growing fintech. With a vibrant work culture that encourages innovation and direct access to leadership, employees benefit from a range of perks including a premium membership to our investor collective and a gym membership. Our commitment to employee growth is evident as you will be at the forefront of building our outbound engine, allowing you to develop your skills in a dynamic environment while contributing to our impressive year-on-year growth.
StudySmarter Expert Advice🤫
We think this is how you could land Data Lead 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 Wave Group!
✨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 Lead at Wave Group.
✨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 Wave Group.
✨Apply Directly through Our Website
When you find a suitable opening like Data Lead at Wave Group, 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 Lead 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 Wave Group, 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 Wave Group. 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 Wave Group
✨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 Wave Group!
✨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.
