Staff Data Scientist

Imagine what you could do here! The people here at Apple dont just create products they build the kind of wonder thats revolutionized entire industries. Its the diversity of those people and their ideas that inspires the innovation that runs through everything we do from amazing technology to industry-leading environmental efforts. At Apple inclusion is a shared responsibility and we work together to foster a culture where everyone belongs and is inspired to do their best on the Apple Store Online team we are responsible for Apples largest store. Our main goal is to deliver a magical personal digital experience where customers can shop buy and learn everything Apple wherever they are. Each customer should feel like they are our only customer and our job is to set the bar for the experience they receive. To run such an extraordinary store it takes extraordinary people and we are looking for someone to help us do extraordinary a Staff Data Scientist you will set the technical direction for AI-powered and personalized experiences across the Retail Online journey. You will architect advanced models define evaluation frameworks for product launches and develop AI-native automated solutions using cutting-edge scientific methods. Operating at the highest technical level you will partner with engineers product and business leaders to drive meaningful customer impact shape long-term technical vision and mentor the next generation of data scientists.

Research and define the gold standard for evaluation methods to improve quality of the Retail online journey. Solve the most ambiguous high-impact analytical problems by applying advanced statistical ML and LLM-driven methodsn- Design execute and oversee robust observational and experimental studies advancing causal inference methodologies across large complex data setsn- Develop AI-native automated solutions to deliver prescriptive insights and proactive alertsn- Drive the feature evaluation philosophy proactively shape the product roadmap with insights and establish a rigorous culture of experimentation

Architect scalable data solutions and AI pipelines to drive exploratory analyses reports experimentation and insights deliverynDevelop and productionize ML models causal inference forecasting anomaly detection attribution and recommendation with ownership of model healthnIntegrate LLMs and Generative AI into core data science workflows (automated EDA synthetic data generation agentic pipelines code acceleration) mitigate hallucinations manage bias in automated pipelines to multiply team upstream data model design define KPI standards at the org level and architect customized data org-level decisions on tooling methodology and data infrastructure partnering with data engineering and ML platform teams. Mentor data scientists and drive team-wide best complex technical findings to executive audiences; develop frameworks that non-technical partners can use. Work independently on sophisticated highly visible projects; develop strategic frameworks.

Masters in Statistics Mathematics Data Science ML Physics Engineering CS or equivalentn5 years of experience as a Data ScientistnExpert proficiency in statistical analysis causal inference experimentation design observational methods (DiD synthetic control IV PSM) drift analysis predictive modeling and heterogeneous treatment effectsnProficiency in SQL Spark or equivalent; Python or R for modeling and analysisnExperience building solutions with LLMs prompt engineering RAG architectures fine-tuning basics and model evaluationnExcellent communication skills product intuition and customer pain point awareness

PhD in Statistics Mathematics Data Science ML Physics Engineering Computer Science or in a quantitative fieldnPublications or patents in causal inference ML or applied statisticsnExperience with causal ML methods (CATE estimation via econml/grf causal forests)nExperience building LLM-powered analytical tools synthetic data generation for training or privacy preservationnExperience with recommendation systems and ML ranking models data architecture data lakes streaming vs. batch and data with MLOps: CI/CD for models Kubernetes feature stores