Staff Data Scientist, Marketing Science ($200k - $300k salary + bonus)

Build the company's marketing measurement system — a Bayesian marketing mix model with adstock and saturation, channel-level response curves and full uncertainty, using PyMC, Stan, NumPyro, Meridian or a framework you can justify

Separate attributed results from true incrementality by designing and analysing geo-experiments, conversion-lift tests, holdouts and synthetic-control studies, using the results to calibrate the model

Translate models into budget decisions by estimating marginal ROAS and marginal CAC by channel, running constrained budget optimisation, and delivering a clear, risk-aware allocation each planning cycle

Model user value through LTV, retention and payback models tied to activation and trading activity, optimising for profit rather than platform-reported ROAS

Develop defensible measurement approaches for channels where lift tooling is thin or missing, such as OpenAI ads, X, Apple Search Ads and crypto-native networks

Collaborate with the Head of Paid Acquisition & Performance Marketing to set test roadmaps, define success metrics, and decide what to scale or stop

Ship production-quality code that is versioned, tested and reproducible, partnering with Data Engineering on the inputs models require

Communicate uncertainty clearly, writing decision memos that founders and operators can act on, including cases where the honest answer is "we don't know yet — here's the test"

Advanced quantitative training — a PhD, MSc or equivalent depth in mathematics, statistics, econometrics, physics or operations research, with the ability to derive methods rather than just apply them

4+ years of hands-on experience (PhD research counts), with a track record building marketing mix or advertising response models that moved real budgets, whether in-house at a consumer, fintech or app business, or at a measurement company such as Recast, Haus, PyMC Labs, Google, Meta or similar, with explainable, evidenced results

Hands-on causal inference experience, having designed, run and analysed incrementality experiments, and knowing how to proceed when the experiment and the model disagree

Deep Bayesian modelling expertise, including hierarchical models, justified priors, identification, posterior diagnostics and the computational skill to make them run efficiently

Strong Python or R skills in production settings, writing model code yourself

Commercial judgment, thinking natively in incremental profit, LTV, CAC and payback, and able to turn a posterior into a recommendation a non-specialist can act on

A scrappy, high-energy operator mindset suited to building the playbook in a fast-paced, resource-lean environment