Marketing Measurement Analyst

As a Growth Analyst you'll be reporting directly to the VP of Performance Marketing and be exposed to Commercial senior leadership. You will be part of the global Performance Marketing team and be responsible for building the measurement infrastructure that drives how we allocate marketing spend.

You'll use your quantitative background to design and run incrementality experiments, maintain and evolve MMM models, and translate complex measurement outputs into clear budget recommendations for senior leadership. If you enjoy working at the intersection of causal inference, marketing science, and data engineering, and want your work to have a clear impact on results,  we're looking for someone like you.

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  Design, execute, and analyze incrementality experiments (geo, holdout, synthetic control) to measure true causal impact of marketing spend

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  Maintain and evolve MMM models to support budget planning and scenario analysis

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  Deliver budget allocation recommendations to senior marketing leadership

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  Build and refine LTV models to inform acquisition and retention strategy

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  Develop automation pipelines for budget pacing and allocation decisions

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  Support data ingestion and ETL processes for marketing platforms (Meta, Google, etc.)

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  Build dashboards and self-serve reporting tools for cross-functional teams

• You have an advanced degree (Master's preferred) in Statistics, Data Science, Economics, Engineering, or a related quantitative field

• You bring 3+ years of experience in marketing analytics, data science, or other quantitative roles or equivalent hands-on experience in marketing measurement

• You have clear knowledge of all topics related to marketing measurement, including MMM, incrementality testing (geo experiments, holdout groups, synthetic control), attribution modeling, attribution and testing

• You're proficient in Python or R for data analysis, statistical modeling, and automation

• You're advanced with SQL and experienced with at least one data visualization tool such as Tableau, Looker, or Google Data Studio

• You're familiar with ETL processes, statistical analysis (hypothesis testing, regression, causal inference), and tools like Robyn, Meridian, PyMC, Prophet, or Scikit-learn

• You have strong statistical foundations, comfortable reasoning across both Bayesian and frequentist frameworks, interpreting uplift, and communicating uncertainty clearly to non-technical stakeholders.

• You can translate complex measurement outputs into clear, actionable recommendations for senior non-technical stakeholders

• You're curious, structured, and able to navigate ambiguity and operate autonomously in a fast-paced environment

• You are fluent in English