Data Scientist, Apple Pay Marketing (Machine Learning Research)

Apple is where individual imaginations gather, committing to 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. This 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 running 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.

Description

We are seeking an experienced Data Scientist with the intellectual curiosity and strategic depth to reimagine how Apple Pay measures and optimizes its marketing. You do not wait to be handed a question. Instead, you identify the questions worth asking, conceptualize the ideal frameworks to answer them, and propose innovative approaches that others have yet to consider. You possess a deep understanding of the marketing and media landscape. You know how marketing mix models quantify cross-channel effectiveness using statistical and econometric techniques. You understand
how incrementality testing, ranging from geo-based experiments to causal inference methods, isolates true causal lift.

Furthermore, you know how behavioral signals derived from clustering, propensity modeling, and sequence analysis can shape smarter audience strategies and campaign designs. What sets you apart is your ability to architect the right measurement framework before a single model is built. You excel at identifying the causal assumptions that must hold, the confounders that must be controlled, and the experimental conditions required to make results actionable.

You leverage Artificial Intelligence and Machine Learning to elevate these frameworks to unprecedented levels of rigor, scale, and speed. This includes building production-grade causal inference pipelines, designing ML-powered experiment analyses, and applying Large Language Models (LLMs) to accelerate how insights are generated and communicated.

Minimum Qualifications

Hands-on experience in marketing science, including building marketing mix models, causal inference, and incrementality measurement.
Proven experience designing and executing rigorous marketing experiments.
Demonstrated proficiency in applying ML techniques to large-scale marketing and customer datasets.
Strong programming skills in Python and data science libraries (such as pandas, NumPy, scikit-learn, and statsmodels).
Advanced command of SQL for querying, manipulating, and analyzing massive marketing and media datasets.
Familiarity with Generative AI and large language models, along with a comfort level in integrating AI tools into daily analytical workflows.
Exceptional written and verbal communication skills, with the ability to tell compelling stories with data to diverse technical and non-technical stakeholders.

Preferred Qualifications

Experience analyzing paid media data across various channels, including paid digital, in-store media, social, and other performance marketing platforms.
Deep understanding of both awareness and performance marketing measurement.
A track record of actively following industry trends in marketing science and media measurement, with a habit of bringing emerging methodologies and tools to the team.
Experience applying Generative AI directly to marketing workflows, such as budget optimization, automated creative analysis, or campaign performance reporting.
Advanced degree (M.S. or Ph.D.) in Statistics, Machine Learning, Econometrics, Marketing Science, or a related quantitative field.