Junior Data Scientist, Fospha

• 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
• 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

Desirable

• 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)
• 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