EA — Growth Analytics — A/B Test & Causal Inference

Role context: Data Scientist, Growth Analytics · Est. study time: 50 min · 5 questions

How these map to the role

These are the games marketing-science methods: MMM, geo incrementality, retargeting holdouts, forecasting, and LTV modeling — the exact toolkit the JD names. Each answer is a coaching walkthrough: a Sample answer (clarify → approach → simulated back-and-forth → clear call), a Deep dive with the math, then a Grading rubric.

The recurring instinct across all five: attribution over-credits — incrementality and MMM are the truth — and value is player LTV, not installs.

Questions (5)

Build a Bayesian hierarchical MMM for EA's marketingCausal inferenceHardMeasure a channel's incrementality without user trackingCausal inferenceHardIsolate retargeting's true lift with a ghost-ad holdoutExperiment designHardForecast installs and revenue with seasonalityForecastingMediumPredict and validate player LTV early enough to bidForecastingHard