Machine Learning Data Scientist Apple Pay Marketing

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 others 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. Its 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 youll do more than join something youll add something. At Apple extraordinary ideas have a way of becoming great products services and customer experiences very quickly.

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 dont wait to be handed a question you identify the questions worth asking conceptualize the right framework to answer them and propose approaches that others havent considered 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 is the tool you bring to take those frameworks to a level of rigor scale and speed that wouldnt 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.

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

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

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