- 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.