Apple is where individual imaginations gather together, committing to the values

that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something. At Apple, extraordinary ideas have a way of becoming great products, services, and customer experiences very quickly.

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DESCRIPTION

We are looking for an experienced Data Scientist with the intellectual curiosity and strategic depth to reimagine how Apple Pay measures and optimizes its marketing. You don't wait to be handed a question — you identify the questions worth asking, conceptualize the right framework to answer them, and propose approaches that others haven't considered yet. You know the marketing and media landscape deeply: how marketing mix models quantify cross-channel marketing effectiveness using statistical or econometrics models, how incrementality testing — from geo-based experiments to causal inference methods — isolates true causal lift, and how behavioral signals derived from clustering, propensity modeling, or sequence analysis can shape smarter audience strategies and campaign design. What sets you apart is the ability to architect the right measurement framework before a single model is built — identifying the causal assumptions that need to hold, the confounders that need to be controlled for, and the experimental conditions that will make results actionable. AI/ML is the tool you bring to take those frameworks to a level of rigor, scale, and speed that wouldn't otherwise be possible — whether that means building production-grade causal inference pipelines, designing ML-powered experiment analysis, or applying LLMs to accelerate how insights are generated and communicated.