Graduate Marketing Scientist - Austin, TX

About the role

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 and CarParts 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. We're looking for a Graduate Marketing Scientist to join Fospha's Marketing Science team in Austin,TX. Fospha builds marketing measurement products for ecommerce brands — attribution, marketing mix modelling, incrementality testing, and brand impact measurement. Marketing Science owns the applied end of that: designing and delivering incrementality tests and MMM engagements for clients, and standing behind the numbers when a client challenges them. This is the entry point into the function, and it is a hands-on one. You'll work on live test and MMM delivery under supervision from the start, and you'll be the first person looking at a client's data when a number doesn't behave the way it should. It's a role for someone who wants to learn causal measurement properly, in a business where it's the product rather than a side project. Team: Marketing Science Level: Graduate — Entry (Data Science Career Development Framework) Location: Austin, TX

Responsibilities

• Assemble and validate test data — geo-level spend and conversion series, checking pre-period parity between treatment and control, spotting the coverage gaps that invalidate a design before it launches
• Support test design under review — market matching and control selection, power and minimum detectable effect sanity checks, and identifying contamination risks such as geo-targeting settings that don't behave the way the platform's documentation claims
• Run analysis and read the results honestly — pre-treatment fit diagnostics, lift estimates with their intervals, and what a null result does and doesn't tell you
• Qualify client data for MMM — spend coverage across channels, whether there's enough variation in spend to identify an effect at all, series length and granularity, collinearity between channels, and gaps that will bias the result
• Assemble and validate model input datasets, and investigate the discrepancies that surface when you do
• Support model runs and read the diagnostics — fit, residuals, convergence, and whether a channel's estimated contribution is plausible
• Contribute to output-extension work under review — building on an existing MMM result, for example forecasting or budget scenario work derived