Data Scientist - #2189697

Data Scientist

London

4 days/week office

Up to £80,000 + bonus

The opportunity

Our client is a fast-growing intelligence business that helps senior Operations and Supply Chain leaders cut through the noise and make better decisions. They combine proprietary data, member research and AI-native analysis to answer the questions that keep C-suite executives up at night.

Right now they’re riding two of the biggest tailwinds in the market — AI and supply chain — which is a big part of why they’ve grown so quickly since raising funding last year. They’re still only around 100 people and firmly in scale-up mode, so roles here are broad rather than tightly boxed in, with real scope to shape what the position becomes. They want people who bring their own ideas about how things should work, not just execute a brief.

Alongside their Data & Insights function, they’re hiring a Data Scientist to help uncover what’s actually moving the needle on AI and digital adoption in supply chains, and turn that into intelligence their members can act on.

What you’ll actually do

A representative project: proving that specific AI, robotics, or technology implementations are actually having a positive commercial impact — for example, sourcing data points from 10-K filings on whether and how a company mentions a given AI, robotics, or technology implementation, combining that with financial performance data, and then running proper correlation and statistical work to find the patterns that are genuinely commercially relevant, not structuring data for its own sake, but using it to answer a real question about what’s driving outcomes.

It’s a fast-paced environment: you’ll typically have several different projects on the go each month rather than sinking weeks into a single piece of work, so it suits someone who likes variety and moving at pace. You’ll work closely with the Analyst and Research side of the team — they’ll sometimes bring you in on the final client-facing narrative, but the analytical thinking behind it, what the data is and isn’t telling you and why it matters, is yours.

What makes someone good at this

This isn’t a software engineering role — there’s a separate (currently paused) AI Engineer seat for that. It’s a data science and insight role: genuinely comfortable with ambiguity and messy data, and more interested in the “why” behind a correlation than in producing one and moving on.

The people who do well here use AI thoughtfully throughout their process — not just to speed up code, but to explore data, test approaches and sanity-check their own conclusions — while knowing exactly where to stop trusting the output and check it themselves.

Most come from a data science background with a genuinely commercial mindset: someone who has always cared more about the decision an analysis supports than the elegance of the model behind it. Self-starters do particularly well here — if you’ve ever looked at a question and gone and built the dataset yourself rather than waiting for a clean one to land in your lap, this role will make sense to you.

What you’ll need

• Around 6-8 years in data science, applied statistics, or a closely related quantitative field — or a shorter track record if you’ve spent time building or running something of your own and are looking to bring that energy into a team
• Strong Python and SQL, with real experience building and maintaining data pipelines and transformations, not just running ad hoc queries
• Hands-on experience extracting structured data from messy or unstructured sources — documents, filings, scraped or third-party data — and getting it into an analysable state
• Genuine statistical and quantitative method: correlation and relationship analysis, feature engineering, transformation logic — data science, not BI reporting
• Comfortable in modern data environments (Databricks or equivalent)
• Hands-on, critical fluency with AI-assisted development and analysis tools — using them to write code, explore data, and sanity-check findings, while always applying your own judgement on what to trust
• Comfort with ambiguity: able to take a loosely scoped question, form your own hypothesis about what’s going on, and iterate toward a defensible answer without needing perfectly defined requirements from the outset
• Supply chain or operations domain exposure is a genuine plus, not required

The package

Up to £80,000 plus a bonus scheme, private healthcare and life insurance, enhanced pension, enhanced parental leave and unlimited holiday. Based from a well appointed central London office four days a week, in a small, high trust team that moves fast, expects a lot, and gives a lot of room to grow into shaping what gets investigated next rather than just answering the brief as given.