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
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
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
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.
We run a published Data Science Career Development Framework with six levels. You'd join at Career, where the expectations are:
AI Fluency & Tooling: Leverages AI to solve coding tickets quickly; consistently unblocks themselves on cross-department dependencies, especially product and engineering; never sends AI-assisted communication without checking quality; expert at using AI to build automation workflows.
Machine Learning & Modelling: Strong ML knowledge across multiple algorithm families; independently chooses and applies the right modelling approach within Fospha's suite.
Engineering & Codebase: Leads on larger coding projects and tickets with production-level code; resolves queries and bugs efficiently and independently; master at using supplied AWS tooling through AI; uses automated QA tools efficiently and flags gaps to the QA team.
Stakeholder & Communication: Confidently and independently communicates with clients and colleagues as a modelling expert; assists with the development of junior members of staff.
Job Complexity: Consistent contributor and respected knowledge holder, with emerging collaboration and mentorship abilities.
Progression to Advanced level is against explicit, published criteria — taking on almost any ticket efficiently, building and planning production repositories, collaborating with product on project outcomes and estimates, actively developing junior colleagues, and representing Fospha as a trusted external voice on modelling in senior client and partner conversations. You'll know what you're working towards from your first week.
Throughout, we look for the same core behaviours: concise communication, collaboration, problem solving, critical thinking, growth mindset, attention to detail, time management, and initiative.
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.
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