--- title: 'Graduate Marketing Scientist at Fospha' canonical: 'https://feeny.ai/job/graduate-marketing-scientist-fospha-london-pwtjfm4ht8fe' type: 'job' last_seen: '2026-09-10' --- # Graduate Marketing Scientist at Fospha - **Company:** Fospha - **Location:** London, United Kingdom - **Posted:** 2026-08-26 - **Last confirmed live:** 2026-09-10 - **Apply:** https://job-boards.greenhouse.io/fosphamarketing/jobs/8146708 ## Job description 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. ## About the role We'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. Team: Marketing Science Level: Graduate — Entry (Data Science Career Development Framework) Location: London ## What you'll do Marketing mix modelling (MMM) & Testing Services - 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 from it - Compare results across methods — where MMM, incrementality, and platform-reported figures disagree, understanding why is the interesting part of the job Model trust and diagnostics - First and second line on client trust queries — investigating why a number changed, working in SQL against client data to isolate the cause - Distinguish 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 underneath - Triage PSPs on model trust, resolve what you can, and escalate what turns out to be a genuine model problem with a clear diagnosis attached - Reconcile 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 inside - Log and tag incidents consistently, so recurring failure patterns become visible and can be automated away rather than repeatedly handled Client communication and enablement - Run templated explainer sessions under review, walking clients through how our measurement works - Draft documentation and presentations above the core explainer content, and feed recurring query themes back into the source material - Fact-check methodology claims in product marketing collateral before it goes out ## What we're looking for We’re looking for someone with a strong foundation in maths and stats with clear communication who is looking to growth their skillset. Technical - Working proficiency in SQL — you can investigate a discrepancy yourself rather than asking someone else to pull the data - Python, 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 means - Some exposure to experimental design — randomisation, control groups, confounding, and why a badly designed test is worse than no test - Strong 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 it Communication - Clear, concise written communication — a large share of this job is explaining something technical to someone who isn't - Composure in client-facing conversation, including when the client is unhappy with a number - Real attention to detail, and the discipline to log things consistently even when it's dull - High agency — you'll be given ownership as fast as you demonstrate you can hold it ## Nice to have - Exposure to marketing, ecommerce, or advertising data - Familiarity with Bayesian methods and/or modelling - Experience with cloud data tooling - Experience presenting to or supporting external stakeholders ## How you'll grow Level doesn't gate what you're allowed to attempt. It scales how much support you get doing it. A Graduate can work on MMM output-extension work; it just carries heavier review than it would for a Mid. Incrementality delivery: Triages test type and routes to Product where self-serve applies. Prepares and validates test data. Supports design and analysis under review. MMM delivery: Qualifies client data. Assembles model inputs. Contributes to output-extension work under heavy review. Model trust & PSPs: First and second line on trust queries. Triages PSPs, attempts resolution, escalates genuine model problems with a diagnosis. Client sessions: Runs templated explainer sessions under review. Documentation & enablement: Drafts documentation and presentations above the core explainer. Feeds recurring query themes back into explainer content. Fact-checks methodology claims in product marketing collateral. Automated trust workflows: Logs and tags trust incidents consistently so recurring failure patterns are visible. Surfaces themes from the query queue into workflow design. Supervision: Detailed instruction; works closely with senior colleagues. Development time protected against product and consultancy workload. Upskilling is funded, not assumed. A named share of entry-tier capacity is held for development work and protected against the support queue. Nobody upskills in the gaps between tickets, so we don't pretend otherwise. The step to Junior is independence: running templated sessions and first-line triage unaccompanied, and resolving the full range of inbound queries with only moderate senior input. The step to Mid runs through one of two routes — owning a piece of MMM output-extension work, or taking a recurring trust problem you already handle manually and turning it into automated monitoring that alerts an account manager before the client notices. Both are genuinely technical, and both are allocated deliberately rather than first-come-first-served. From Mid, you own MMM and incrementality delivery with senior sign-off. ## Why Fospha - Causal measurement is the product. Geo experiments, MMM, difference-in-differences, and Bayesian attribution are what the business sells. You'll be working on them, not adjacent to them. - Live client work early. You'll be on real test and MMM delivery in your first months, with review rather than distance. - Structured support, not sink-or-swim. Session allocation rules, a maintained escalation list, and protected development time exist so that being new isn't a liability. - A published ladder. You'll know what the next level requires and what evidences readiness for it from your first week. ## About Fospha ## Company Overview - **One-liner**: Fospha provides a full-funnel marketing measurement operating system for retail commerce, enabling brands to measure and optimise the impact of every impression and click across channels and marketplaces. - **Entity Type**: Private (backed by venture builder Blenheim Chalcot; post-seed/revenue-stage with 200+ customers) - **Headquarters**: London, United Kingdom (with offices in the US and across three continents) - **Founded**: 2014 - **Founders**: Founded by Blenheim Chalcot; led by CEO Sam Carter ## Core Business - **Primary industry**: Marketing measurement & analytics (SaaS for retail commerce) - **Target customers**: B2B – mid-market to enterprise retail brands (DTC, Amazon, TikTok Shop, etc.) such as Gymshark, Huel, and Dyson - **Mission or purpose**: To give brands the confidence to spend their full-funnel budgets and drive long-term profitable growth by providing transparent, privacy-safe, daily measurement. ## Products & Services - **[Daily MMM (Core & Beam)](https://www.fospha.com/)**: Always-on Media Mix Model delivering impression-led, full-funnel measurement at daily and ad-level granularity. Deterministic, correlative, and causal models working together (ensemble methodology). - **[Prism – Automated Budget Optimisation](https://www.fospha.com/)**: Bayesian saturation curves and marginal return forecasts that automatically shift budgets away from underperforming campaigns, improving channel efficiency. - **[Halo – Marketplace Measurement](https://www.fospha.com/)**: Incremental models that quantify how paid media drives sales across Amazon, TikTok Shop, and retail marketplaces, capturing cross-channel halo effects. - **[Glow – Brand Measurement](https://www.fospha.com/)**: Causal reasoning models to prove the revenue impact of brand-building spend. - **[Fospha AI](https://www.fospha.com/)**: In-dashboard conversational AI (marketing strategist) and MCP integration allowing teams to query data via Claude, ChatGPT, etc. - **[Science & Strategy Squad](https://www.fospha.com/about-fospha)**: Dedicated measurement experts running incrementality tests, standalone MMMs, and setting the learning agenda. ## Market Standing - **Valuation/Market Cap**: Not publicly disclosed - **Key Metric**: Over $2.5 billion in marketing spend under management (as of 2025); 200+ customers across 26 countries - **Notable Investors/Partners**: Founded and backed by **Blenheim Chalcot**; certified partner of **Meta, TikTok, and Snapchat**; customers include Gymshark, Huel, Dyson - **Growth Signals**: - 150+ employees across three continents (London, US, and other offices) - $2.5bn spend under management (up from earlier $4bn claim – likely cumulative milestone) - Acquisitions: iJento (web analytics, 2016) and another unnamed acquisition - Expanded to 200+ customers and three continents by 2025 - Launched Fospha Pro (full-funnel MTA/MMM + predictive planning) and integrated AI capabilities ## Competitive Advantages - **Glass-box transparency**: Every model layer, validation metric, and decision rule is visible to clients – no black boxes. - **Privacy-first from day one**: Moved away from pixel-based tracking over a decade ago; compliant with GDPR, CCPA, iOS14, and preparing for Google’s Privacy Sandbox. - **Ensemble methodology**: Combines deterministic click modelling, impression modelling, Bayesian saturation curves, and causal reasoning in a single always-on system. - **Daily, ad-level granularity**: Unlike legacy MMMs that run quarterly, Fospha delivers full-funnel results daily at the ad level, enabling near-real-time optimisation. - **Marketplace coverage**: Uniquely measures cross-channel halo effects on platforms like Amazon and TikTok Shop, which pixel-based tools miss. ## Strategic Focus - **Current priorities**: Deepening automation (Prism), expanding AI capabilities (Fospha AI and MCP integrations), and strengthening marketplace measurement (Halo). Scaling globally with a growing customer success and sales team. - **Direction for growth**: Continue to replace legacy attribution and MMM tools in retail commerce; add more retail media networks and sales channels; increase spend under management and headcount. ## Why Work Here - **Culture highlights**: Values include “Candour with caring”, “Seek inclusion & diversity”, “Customer at the heart”, and “Work hard, work well, work together”. Emphasis on continuous learning, professional growth, and collaborative innovation. - **Remote/hybrid/office policy**: Offices in London (HQ), Austin (US), and other locations. Roles span across Customer Success, Sales, Marketing Science, Product, and Central Operations. Likely hybrid/flexible depending on role (exact policy not specified but offices are active). - **Notable perks**: Opportunity to work with a team of 150+ data scientists and marketing experts; impact on billion-dollar spend decisions; close partnerships with major ad platforms; dynamic growth phase with significant career advancement potential. ## Sources 1. [fospha.com](https://www.fospha.com/) 2. [fospha.com/about-fospha](https://www.fospha.com/about-fospha) 3. [fospha.com/life-at-fospha](https://www.fospha.com/life-at-fospha) 4. [cbinsights.com](https://www.cbinsights.com/company/fospha) 5. 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