Data Scientist

At Apple, we believe extraordinary products are built through deep understanding, rigorous analysis, and relentless focus on quality. We are seeking an exceptional Data Scientist to lead algorithm evaluation and performance intelligence for next-generation intelligent systems.\\n\\nIn this highly visible technical role, you will define how algorithm quality is measured, understood, and improved. You will drive evaluation methodologies, establish scalable metrics frameworks, and lead deep technical investigations into algorithm behavior, failure modes, and system performance. Working at the intersection of machine learning, data science, and product quality, you will influence critical decisions through data-driven insights and technical leadership.\\n\\nYou will collaborate closely with algorithm engineers, machine learning researchers, QA, annotation teams, and cross-functional partners to shape evaluation strategy and improve the robustness, reliability, and customer experience of intelligent systems at scale. This role also requires identifying opportunities to leverage agentic systems and AI-assisted workflows to improve efficiency, scalability, and technical depth in evaluation and analysis.

As a Data Scientist focused on Algorithm Evaluation, you will serve as a technical leader responsible for driving end-to-end evaluation strategy for complex algorithmic systems. You will develop rigorous methodologies to assess algorithm quality, identify failure patterns, and quantify system behavior across large-scale datasets and real-world scenarios.\n\nYou will lead deep dives into algorithm performance, uncover insights through advanced statistical analysis, and establish scalable frameworks to improve evaluation efficiency and confidence in product decisions. You will also help shape how agentic solutions and AI-assisted tooling are integrated into day-to-day workflows to accelerate data analysis, failure investigation, annotation quality improvement, root-cause discovery, and evaluation automation.\n\nThis role requires strong technical depth, exceptional analytical rigor, and the ability to influence cross-functional teams in highly ambiguous environments.

BS and a minimum of 10 years relevant industry experience\n7+ years of experience in data science, machine learning evaluation, algorithm analysis, or related technical disciplines.\nDemonstrated experience driving technical initiatives in ambiguous, cross-functional environments.\nStrong expertise in statistical analysis, experimentation methodologies, and large-scale data analytics.\nDeep experience evaluating machine learning, computer vision, or AI systems through quantitative metrics and performance analysis.\nStrong programming experience in Python, with hands-on experience building scalable analytics and automation pipelines.\nExperience conducting algorithm deep dives, failure analysis, and model performance investigations.\nFamiliarity with AI-assisted analysis workflows, foundation models, agentic systems, or intelligent automation approaches for technical problem solving.\nStrong understanding of algorithm evaluation concepts, including precision/recall tradeoffs, confusion analysis, robustness measurement, regression detection, and benchmarking methodologies.\nExceptional problem-solving skills with ability to translate ambiguous technical problems into measurable frameworks.

Experience evaluating machine learning, computer vision, multimodal, or foundation model systems in production environments.\nExperience designing or deploying agentic workflows to improve engineering productivity, data analysis, evaluation efficiency, or annotation quality.\nFamiliarity with LLM-based systems, retrieval pipelines, structured reasoning, or AI-assisted analytics frameworks.\nExperience defining quality frameworks and evaluation methodologies for large-scale intelligent systems.\nExperience building automated benchmarking systems and large-scale performance monitoring infrastructure.\nKnowledge of A/B experimentation, causal inference, and advanced statistical modeling.\nStrong understanding of the ML lifecycle, model validation, and continuous evaluation methodologies.\nExcellent communication skills with proven ability to influence technical decisions through data-driven insights.