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.