Junior Data Scientist

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

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

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.

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.