Fospha

Graduate Marketing Scientist role using SQL, Python, statistics, experimentation, and causal measurement to analyse ecommerce marketing data and deliver insights for clients.Fospha is dedicated to building the world's most powerful measurement solution for online retail. For over a decade, we've helped teams make smarter decisions with full-funnel marketing insights, forecasting, and optimisation. With Fospha, every team moves faster and grows smarter. About the roleWe're looking for a Graduate Marketing Scientist to join Fospha's Marketing Science team in London.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.What you'll doMarketing mix modelling (MMM) & Testing ServicesAssemble 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 launchesSupport 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 claimsRun 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 youQualify 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 resultAssemble and validate model input datasets, and investigate the discrepancies that surface when you doSupport model runs and read the diagnostics — fit, residuals, convergence, and whether a channel's estimated contribution is plausibleContribute to output-extension work under review — building on an existing MMM result, for example forecasting or budget scenario work derived from itCompare results across methods — where MMM, incrementality, and platform-reported figures disagree, understanding why is the interesting part of the jobModel trust and diagnosticsFirst and second line on client trust queries — investigating why a number changed, working in SQL against client data to isolate the causeDistinguish a bug from a methodology change — attribution window changes, model recalibration, data feed gaps, and platform reporting shifts all look similar from the outside and have very different signatures underneathTriage PSPs on model trust, resolve what you can, and escalate what turns out to be a genuine model problem with a clear diagnosis attachedReconcile platform-reported figures against our measurement — why walled-garden ROAS disagrees with ours is the hardest recurring question in the business, and you'll be learning it from the insideLog and tag incidents consistently, so recurring failure patterns become visible and can be automated away rather than repeatedly handledClient communication and enablementRun templated explainer sessions under review, walking clients through how our measurement worksDraft documentation and presentations above the core explainer content, and feed recurring query themes back into the source materialFact-check methodology claims in product marketing collateral before it goes outWhat we're looking forWe’re looking for someone with a strong foundation in maths and stats with clear communication who is looking to growth their skillset.TechnicalWorking proficiency in SQL — you can investigate a discrepancy yourself rather than asking someone else to pull the dataPython, or a demonstrated ability to pick it up quickly. Most of our analysis tooling sits there.Grounding in inferential statistics — hypothesis testing, uncertainty, statistical power, and what a null result meansSome exposure to experimental design — randomisation, control groups, confounding, and why a badly designed test is worse than no testStrong AI fluency — you use AI tools to get moving on unfamiliar problems and plug gaps in your own knowledge, and you QA the output before you rely on itCommunicationClear, concise written communication — a large share of this job is explaining something technical to someone who isn'tComposure in client-facing conversation, including when the client is unhappy with a numberReal attention to detail, and the discipline to log things consistently even when it's dullHigh agency — you'll be given ownership as fast as you demonstrate you can hold itNice to haveExposure to marketing, ecommerce, or advertising dataFamiliarity with Bayesian methods and/or modellingExperience with cloud data toolingExperience presenting to or supporting external stakeholders

Posted 8 days ago