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 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
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.
