Staff Data Scientist, Marketing Science

About the role

In this role, you will build and refine a marketing measurement system focused on Bayesian modeling techniques. Your work will directly influence budget allocations for paid acquisition, ensuring that decisions are data-driven and aligned with profitability goals.

You will collaborate closely with the Head of Paid Acquisition & Performance Marketing to define test roadmaps and success metrics. Success in this position means delivering clear, actionable insights that help the team navigate the complexities of marketing in a fast-paced environment.

The salary range for this role is between $200,000 and $300,000, depending on experience and scope.

Skills that matter here

• Python: You will write production-quality code in Python to implement your models.
• R: R may be used alongside Python for statistical analysis and modeling.
• Bayesian modeling: This role emphasizes the use of Bayesian techniques to develop marketing mix models.
• marketing mix models: You will create and optimize models that assess the effectiveness of different marketing channels.
• causal inference: Hands-on experience with causal inference is crucial for designing and analyzing experiments.

Who this role suits

• You have a strong quantitative background, ideally with a PhD or equivalent in a relevant field.
• You thrive in fast-paced environments and are comfortable with ambiguity and limited resources.
• You possess a scrappy, high-energy mindset and enjoy building processes from the ground up.
• You can effectively communicate complex statistical concepts to non-specialists.

From the employer

What You'll Do

• 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."

What We're Looking For

• 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.

Compensation & Benefits

• Base salary: $200,000 - $300,000, depending on experience and scope.
• Performance-related compensation: Generous performance bonus; equity or token participation depending on level.
• Benefits and relocation support: To be confirmed for this role.