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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist, Apple Pay Marketing (Machine Learning Research) - **Company:** Apple Inc. - **Location:** Cupertino, CA, United States - **Salary:** $150,400.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Cluster Analysis, Computer Programming, Python (Programming Language), Machine Learning, Automation of Marketing, NumPy, SQL Databases, Large Language Models, Generative AI, Pandas, Scikit Learn, Statistics Packages - **Published:** September 1, 2026 - **Apply:** https://www.themuse.com/jobs/apple/data-scientist-apple-pay-marketing-machine-learning-research ## About the Role 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. 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. ## 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. 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, translating complex results into concrete, actionable campaign recommendations. Apply advanced 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. Architect 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 compelling visualizations, narratives, and recommendations. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Vectorize all the things! 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