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

Responsibilities

• Build our marketing measurement system - a Bayesian marketing mix model with adstock and saturation, channel-level response curves and full uncertainty, in PyMC, Stan, NumPyro, Meridian or a framework you can defend
• Separate attributed from incremental - design and analyse geo-experiments, conversion-lift tests, holdouts and synthetic-control studies, and use the results to calibrate the model
• Turn models into budget decisions - estimate marginal ROAS and marginal CAC by channel, run constrained budget optimisation and give the paid acquisition team a clear, risk-aware allocation every planning cycle
• Model what a user is worth - build LTV, retention and payback models tied to activation and trading activity, so we optimise for profit, not platform-reported ROAS
• Measure what the platforms can't - build defensible approaches for newer or opaque channels (OpenAI ads, X, Apple Search Ads, crypto-native networks) where lift tooling is thin or missing
• Work alongside the Head of Paid Acquisition & Performance Marketing to set test roadmaps, define success metrics and decide what to scale and what to stop
• Ship production-quality code - versioned, tested and reproducible - and partner with Data Engineering on the inputs the models need
• Explain uncertainty plainly - write decision memos that founders and operators can act on, including when the right answer is "we don't know yet - here's the test"

Requirements

• Advanced quantitative training - a PhD, MSc or equivalent depth in mathematics, statistics, econometrics, physics or operations research. What matters is that you can derive it, not just run it
• 4+ years of hands-on experience (PhD research counts), with a track record building marketing mix or advertising response models that moved real budgets - in-house at a consumer, fintech or app business, or at a measurement company (Recast, Haus, PyMC Labs, Google, Meta or similar) - with results you can explain and evidence
• Hands-on causal inference - you have designed, run and analysed incrementality experiments, and you know what to do when the experiment and the model disagree
• Deep Bayesian modelling - hierarchical models, priors you can justify, identification, posterior diagnostics and the computational know-how to make them fit at speed
• Strong Python or R in production - you write the model code yourself
• Commercial judgment - you think natively in incremental profit, LTV, CAC and payback, and you can turn a posterior into a recommendation a non-specialist will act on
• A scrappy, high-energy operator mindset - you thrive in a fast-paced, resource-lean environment where you're building the playbook as you go

Preferred

• Contributions to open-source measurement tools - PyMC-Marketing, Meridian, Robyn, LightweightMMM, CausalPy, Stan, GeoLift or similar
• Mobile app user acquisition - SKAdNetwork, AppsFlyer or Adjust, and Apple Search Ads
• Crypto experience - as a retail trader, or through work at a crypto startup or larger crypto company

Benefits

• Benefits and relocation support: To be confirmed for this role