Marketing Scientist

Fospha is the measurement system enterprise retail and ecommerce brands run their business on. We give marketing teams one clear, daily view of what's actually working - across every channel and everywhere they sell, from their website to Amazon and TikTok Shop - down to the level of a single ad or piece of creative. It replaces guesswork and gut feel with a number marketing, finance and agencies can all trust and act on.

Brands including Dyson, Gymshark, Adanola and Huel use Fospha up to 25 times a day to decide where budget should move next. We've spent over a decade building this, with more than $40 billion in marketing spend now optimised through the platform - and we're scaling fast across London, Mumbai and Austin.

 Fospha is pioneering the future of marketing intelligence, moving enterprise retailers beyond the broken world of click-based attribution. Our mission is to transform complex data into the single, most trusted source of truth for CMOs and CFOs managing high-stakes budgets.

As a Marketing Science, your focus will be acting as the technical authority for our Enterprise tier, serving as a dedicated conduit between our Customers, Customer Teams, Data Science, and Product teams.

We are looking for a rigorous, analytical practitioner ready to operationalize advanced modelling into tangible business decisions, driving sustainable growth for clients like Huel, Dyson, and Gymshark. You will operate as a post-sales, value-delivery engine embedded directly within the Customer Success department.  

• →Client-Facing Support: Proactively and reactively support Customer Success and Account Management teams on model trust and methodology escalations, directly combating objections related to competitors like Triple Whale or Northbeam.
• →Incrementality & Learning Agendas: Act as the primary Marketing Science Strategist, designing first-party incrementality tests, calibrating models, and building structured learning agendas for clients.
• →R&D "Mouthpiece": Systematically capture and triage field feedback and methodological objections, feeding structured intelligence back to our Product and Data Science teams.
• →Collateral & Knowledge Creation: Build the definitive technical collateral, Academy content, and internal training to raise the baseline data literacy of our entire Go-To-Market organization.