Machine Learning Data Scientist - Apple Pay Marketing

Summary

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.

Minimum Qualifications

Hands-on experience in marketing science — including building marketing mix models, causal inference, and incrementality measurement Experience designing and executing marketing experiments Proficiency in applying ML techniques to marketing and customer datasets Strong proficiency in Python and data science libraries (pandas, NumPy, scikit-learn, statsmodels, or equivalent) Strong command of SQL for querying and analyzing large-scale marketing and media datasets

Preferred Qualifications

Experience with paid media data across channels — paid digital, in-store media, social, and other performance marketing platforms Experience with awareness and performance marketing measurement Actively follows industry trends in marketing science and media measurement — aware of emerging methodologies and tools and brings those perspectives into the team Experience applying Generative AI to marketing workflows — including budget optimization, automated creative analysis, or campaign performance reporting Advanced degree (M.S. or Ph.D.) in Statistics, Machine Learning, Econometric, Marketing Science, or a related quantitative field Strong written and verbal communication skills — able to tell compelling stories with data to both technical and non-technical audiences

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.

Responsibilities

Design and implement marketing mix models and causal inference pipelines that quantify marketing effectiveness and inform budget allocation decisions Build and execute incrementality tests and translate results into concrete campaign recommendations Apply ML techniques such as segmentation, propensity modeling, and behavioral pattern recognition to identify customer response patterns and inform audience strategy and experiment design Partner with cross-functional teams to scope analytical problems, define success metrics, and deliver data-driven recommendations Build and maintain production-grade ML models and workflows that support ongoing marketing measurement and optimization Leverage Generative AI and LLM-based tools to accelerate insight generation, automate reporting workflows, and streamline day-to-day analytical tasks Communicate model outputs and experiment results clearly to both technical and non-technical audiences through visualizations, narratives, and recommendations