Senior Data Scientist - Special Projects

Our team works at the intersection of hardware, software, and intelligence. We design the systems, infrastructure, and tools that enable Apple’s next generation of AI-driven experiences — from on-device middleware and distributed inference platforms to large-scale data pipelines, interactive analytics, and advanced developer tooling. We collaborate closely with hardware, robotics, ML, design, and platform teams to build end-to-end solutions that are performant, intuitive, and deeply integrated into Apple’s ecosystem. The work is hands-on, highly cross-disciplinary, and central to shaping how Apple’s intelligent systems evolve. Proven experience in data science, analytics engineering, or applied research roles Strong proficiency in Python and common data analysis libraries Expertise with data visualization tools or frameworks for building interactive dashboards Experience querying and modeling data in SQL and NoSQL environments Ability to analyze large-scale, high-dimensional, and multi-modal datasets Familiarity with designing or using search and retrieval systems for large-scale data Experience designing KPIs, evaluation metrics, or experiment analyses for complex systems Strong statistical intuition and experience with exploratory data analysis and hypothesis testing Ability to translate complex findings into clear, actionable insights for cross-functional teams Bachelor’s or Master’s degree in Data Science, Computer Science, Applied Mathematics, or related field, and 5+ years of industry experience Experience working with data from distributed or real-time systems, robotics, or multi-modal AI Familiarity with information retrieval techniques (ranking, embedding-based search, relevance modeling) Experience collaborating with ML teams on evaluation methodologies, dataset design, or model diagnostics Knowledge of metadata management systems, cataloging approaches, or data documentation strategies Background in statistical modeling, time-series analysis, or anomaly detection Experience with visualization frameworks used for large datasets Understanding of cloud-scale data processing and distributed compute frameworks