Blenheim Chalcot - Data Scientist

• Essential • Solid commercial data science experience — typically 3–5 years, with a track record of shipping models into production • Strong ML knowledge across multiple algorithm families, and the judgement to pick the right approach for the problem rather than the one you know best • Strong ML knowledge across multiple algorithm families • Strong Python and SQL, with the ability to lead on production-level code and set the standard others work to • Python • Able to debug and resolve issues independently across unfamiliar repositories, using AI tooling to accelerate rather than to guess, while still understanding the problem fully • debug and resolve issues independently • Strong AI fluency — you solve tickets quickly with it, you unblock cross-department dependencies with it, you build automation workflows with it, and you never send AI-assisted output without checking it • Strong AI fluency • Confident, independent communication with clients and stakeholders as the technical authority on the work • Emerging mentorship — you're ready to develop junior colleagues, and you want to • Emerging mentorship • Initiative in accepting and planning your own work, rather than waiting to be allocated it • Genuine attention to detail — much of this work involves noticing when a number is wrong • Bayesian modelling experience, particularly hierarchical models • Experience with AWS or comparable cloud tooling • Familiarity with automated QA tooling and test coverage practices • Experience with marketing, ecommerce, or advertising data • Experience with attribution methodology, MMM, incrementality testing, or Bayesian modelling is genuinely an advantage at this level — but it isn't a filter. Our stack takes time to learn regardless of what you arrive with, and we'd rather hire strong modelling judgement and teach the domain. • How you'll grow • How you'll grow • We run a published Data Science Career Development Framework with six levels. You'd join at Career, where the expectations are: • AI Fluency & Tooling: Leverages AI to solve coding tickets quickly; consistently unblocks themselves on cross-department dependencies, especially product and engineering; never sends AI-assisted communication without checking quality; expert at using AI to build automation workflows. • AI Fluency & Tooling: • Machine Learning & Modelling: Strong ML knowledge across multiple algorithm families; independently chooses and applies the right modelling approach within Fospha's suite. • Machine Learning & Modelling: • Engineering & Codebase: Leads on larger coding projects and tickets with production-level code; resolves queries and bugs efficiently and independently; master at using supplied AWS tooling through AI; uses automated QA tools efficiently and flags gaps to the QA team. • Engineering & Codebase: • Stakeholder & Communication: Confidently and independently communicates with clients and colleagues as a modelling expert; assists with the development of junior members of staff. • Stakeholder & Communication: • Job Complexity: Consistent contributor and respected knowledge holder, with emerging collaboration and mentorship abilities. • Job Complexity: • Supervision: Demonstrates initiative in accepting and planning work. • Supervision: • Progression to Advanced level is against explicit, published criteria — taking on almost any ticket efficiently, building and planning production repositories, collaborating with product on project outcomes and estimates, actively developing junior colleagues, and representing Fospha as a trusted external voice on modelling in senior client and partner conversations. You'll know what you're working towards from your first week. • Advanced • Throughout, we look for the same core behaviours: concise communication, collaboration, problem solving, critical thinking, growth mindset, attention to detail, time management, and initiative.