Data Scientist at Blenheim Chalcot in London

Job description

• We're looking for a Data Scientist to join Fospha's Data Science team in London.
• Fospha builds marketing measurement products for ecommerce brands — attribution, marketing mix modelling, incrementality testing, and brand impact measurement. Our Data Science team owns the models behind all of it, from methodology through to production code. You will work across our technical stacks and own maintaining and growing the codebases that power our product and solutions.
• This role suits an established data scientist who wants to own things properly. You'll lead larger production projects, choose the modelling approach rather than being handed it, and talk to clients as the modelling expert in the room — supported by a team that reviews each other's work seriously, and a company that rewards high agency with ownership.
• Team: Data Science
• Level: Career (Data Science Career Development Framework)
• Location: London

Responsibilities

• Lead larger production projects end to end — scoping, building, and shipping production-level code, rather than working through tickets someone else has specified
• Choose the modelling approach — independently selecting and applying the right method within our suite, across attribution, marketing mix modelling, incrementality testing, and brand impact. You will be given time to keep up-to-date on the latest methodologies in the industry
• Resolve bugs and queries independently — including in parts of the codebase you didn't write, without needing to route them upwards
• Use AI as leverage, not as a crutch — solving coding tickets quickly, unblocking yourself on product and engineering dependencies, and building automation workflows that save the team time
• Work with QA properly — using our automated tooling efficiently and flagging the gaps in it rather than working around them
• Communicate as a modelling expert — confidently and independently, with clients and with colleagues, including when the message is that a number they like is wrong
• Help develop the people around you — code review, methodology critique, and hands-on support for less experienced colleagues. Breaking down tickets and helping those more junior is essential

Requirements

• Essential
• Solid commercial data science experience — typically 3–5 years, with a track record of shipping models into production
• Strong ML knowledge across multiple algorithm families, and the judgement to pick the right approach for the problem rather than the one you know best
• Strong Python and SQL, with the ability to lead on production-level code and set the standard others work to
• Able to debug and resolve issues independently across unfamiliar repositories, using AI tooling to accelerate rather than to guess, while still understanding the problem fully
• Strong AI fluency — you solve tickets quickly with it, you unblock cross-department dependencies with it, you build automation workflows with it, and you never send AI-assisted output without checking it
• Confident, independent communication with clients and stakeholders as the technical authority on the work
• Emerging mentorship — you're ready to develop junior colleagues, and you want to
• Initiative in accepting and planning your own work, rather than waiting to be allocated it
• Genuine attention to detail — much of this work involves noticing when a number is wrong
• Nice to have
• Bayesian modelling experience, particularly hierarchical models
• Experience with AWS or comparable cloud tooling
• Familiarity with automated QA tooling and test coverage practices
• Experience with marketing, ecommerce, or advertising data
• Not required
• Experience with attribution methodology, MMM, incrementality testing, or Bayesian modelling is genuinely an advantage at this level — but it isn't a filter. Our stack takes time to learn regardless of what you arrive with, and we'd rather hire strong modelling judgement and teach the domain.

Skills

Required

• Strong Python and SQL
• AI fluency
• Confident, independent communication
• Mentorship
• Initiative
• Attention to detail

Benefits

• Real methodological depth. Bayesian attribution, MMM, geo lift testing, and causal inference are our day job, not a side project. You'll be working on all of it, not adjacent to it.
• Ownership at the methodology level. At this level you're choosing the approach, not implementing someone else's choice.
• Published career framework. No guessing what the next level requires or when you'll get there.
• A team that reviews each other's work properly. Code review and methodology critique from people who care about getting the model right.
• Direct client impact. The models you build and maintain drive real budget decisions at brands you'll recognise.