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

Responsibilities

We're making a founding hire: a Staff Data Scientist in Marketing Science to own the mathematics behind our paid growth - the causal measurement, the response curves, the customer-value models and the optimisation that decide where our multi-million-dollar, always-on acquisition budget goes across Google, Apple Search Ads, OpenAI ads, Meta, TikTok and X. There is no inherited methodology - you design it from first principles.
Your job is to answer one question, continuously and with evidence: where should the next $1 million go to generate the most additional profit - and how confident are we?
This is a hands-on, builder role. You'll write the models yourself, design the experiments that keep them honest, and sit next to the people moving the budget. Our data engineers own the pipelines; you own the science.
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"

Qualification

Advanced quantitative training - a PhDA scrappyCrypto experience - as a retail traderSophisticated equations matterHOW WE'LL MEASURE SUCCESSFirst 30 daysFirst 90 daysFirst 180 days

Required

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
Sophisticated equations matter. Better allocation decisions matter more.
HOW WE'LL MEASURE SUCCESS
First 30 days: Current measurement audited, a baseline response model live for core channels, and the first incrementality tests designed with the paid acquisition team
First 90 days: An experiment-calibrated model producing channel response curves, marginal returns and a recommended allocation with confidence ranges, used in the budget cycle
First 180 days: Budget reallocations driven by the model delivering measurable incremental profit, with an always-on test-and-learn programme across existing and new channels

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

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Batoncorporation

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