Fospha is dedicated to building the world's most powerful measurement solution for online retail. For over a decade, we've helped teams make smarter decisions with full-funnel marketing insights, forecasting, and optimisation. With Fospha, every team moves faster and grows smarter.
About the role
We're looking for a Data Scientist to join Fospha's Data Science team in London, working within our Stream 3 workstream.
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 have the chance to work across our technical stack to make real code impacts on codebases.
This role suits someone with some commercial data science experience behind them who wants to go deeper. You’ll take on interesting but challenging work with a supportive team structure and a company that rewards high agency with ownership.
Team: Data Science
Level: Developing (Data Science Career Development Framework)
Location: London
What you'll do
- Own moderately difficult tickets end to end — analytical investigations, model backtesting, production bug fixes, and pipeline work, with decreasing need for step-by-step direction
- Debug systematically across our codebases — including repositories you're only partly familiar with, using AI tooling to get up to speed quickly
- Write and review production code — Python and SQL that avoids technical debt, plus reviewing AI-assisted code so it lands with minimal bugs
- Use our QA automation and cloud tooling effectively — running validation properly and understanding the data science parts of our AWS pipelines
- Communicate with clients and colleagues — explaining methodology and findings directly, with only minor assistance from senior colleagues
- Push for clarity up front — working directly with other teams to pin down acceptance criteria and requirements, rather than sitting blocked waiting on them
- Start becoming an internal data science champion — the person other teams come to on the areas you own
What we're looking for
Essential
- Some commercial data science experience — typically 1–2 years, or a strong placement/internship record alongside a quantitative degree
- Strong Python and SQL, with the ability to collaborate on shared code and avoid technical debt
- Deep knowledge of a handful of machine learning algorithms — not breadth for its own sake, but real understanding of a few methods and when they apply
- Able to systematically debug unfamiliar code, using AI tooling to accelerate rather than to guess while still understanding problem fully
- Strong AI fluency — you understand how to optimally start a task with AI, you use it to unblock cross-team dependencies, and you always QA the output for accuracy and brevity before it goes anywhere
- Confident completing work independently once scope is agreed
- Able to communicate with clients and colleagues with only minor assistance
- Genuine attention to detail — much of this work involves noticing when a number is wrong
Nice to have
- Experience with AWS or comparable cloud tooling
- Familiarity with automated QA tooling and test coverage practices
- Any experience with marketing, ecommerce, or advertising data
- Exposure to Bayesian methods, marketing mix modelling, or experimental design
Not required
You do not need prior experience with marketing mix modelling, attribution methodology, incrementality testing, or Bayesian modelling. These are taught here, and we'd rather hire someone who learns fast than someone who arrives pre-loaded.
How you'll grow
We run a published Data Science Career Development Framework with six levels. You'd join at Developing, where the expectations are:
AI Fluency & Tooling: Understands how to optimally start all relevant tasks with AI; confident completing work independently; uses AI to push for acceptance criteria and limit cross-department dependencies; always QAs AI output for accuracy and brevity.
Machine Learning & Modelling: Deep knowledge of a handful of algorithms, and familiarity with the full Fospha model suite.
Engineering & Codebase: Systematically debugs issues with AI support, even in partly familiar repositories; uses supplied AWS tooling efficiently and understands the data science parts of the pipelines; uses existing QA automation effectively; avoids technical debt and collaborates well on code; reviews AI-assisted code so it ships with minimal bugs.
Stakeholder & Communication: Communicates with clients and colleagues with minor assistance.
Job Complexity: Undertakes moderately difficult tickets while starting to become an internal data science champion.
Supervision: Receives detailed instruction, but becomes progressively less dependent on senior colleagues.
Progression to Career level is against explicit, published criteria — leading larger production projects, resolving bugs independently, and communicating as a modelling expert in your own right. 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.
Why Fospha
- 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.
- 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.