Staff Data Scientist

Imagine what you could do here! The people here at Apple don’t just create products — they build the kind of wonder that’s revolutionized entire industries. It’s 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 work.

Here on the Apple Store Online team, we are responsible for Apple’s 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 things.

As 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 methods
- Design, execute, and oversee robust observational and experimental studies, advancing causal inference methodologies across large, complex data sets
- Develop AI-native automated solutions to deliver prescriptive insights and proactive alerts
- Drive the feature evaluation philosophy, proactively shape the product roadmap with insights, and establish a rigorous culture of experimentation

Responsibilities

• Architect scalable data solutions and AI pipelines to drive exploratory analyses, reports, experimentation and insights delivery
• Develop and productionize ML models, causal inference, forecasting, anomaly detection, attribution, and recommendation with ownership of model health
• Integrate 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 output.
• Influence upstream data model design, define KPI standards at the org level, and architect customized data solutions.
• Drive 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 practices.
• Communicate complex technical findings to executive audiences; develop frameworks that non-technical partners can use. Work independently on sophisticated, highly visible projects; develop strategic frameworks.

• Minimum Qualifications
  • Masters in Statistics, Mathematics, Data Science, ML, Physics, Engineering, CS or equivalent
  • 5+ years of experience as a Data Scientist
  • Expert proficiency in statistical analysis, causal inference, experimentation design, observational methods (DiD, synthetic control, IV, PSM), drift analysis, predictive modeling and heterogeneous treatment effects
  • Proficiency in SQL, Spark or equivalent; Python or R for modeling and analysis
  • Experience building solutions with LLMs prompt engineering, RAG architectures, fine-tuning basics, and model evaluation
  • Excellent communication skills, product intuition and customer pain point awareness


• Preferred Qualifications
  • PhD in Statistics, Mathematics, Data Science, ML, Physics, Engineering, Computer Science or in a quantitative field
  • Publications or patents in causal inference, ML, or applied statistics
  • Experience with causal ML methods (CATE estimation via econml/grf, causal forests)
  • Experience building LLM-powered analytical tools, synthetic data generation for training or privacy preservation
  • Experience with recommendation systems and ML ranking models, data architecture, data lakes, streaming vs. batch, and data contracts.
  • Familiar with MLOps: CI/CD for models, Kubernetes, feature stores