Quality Engineer Statistical Analysis, Python & R | SPC & Exploratory Data analysis | Feature Engin

Implementing Fab lot disposition for materials affected by planned or unplanned deviations (e.g., Special Work Requests, Fab Conversions, Quality Deviations, and Excursions). Managing disposition operations across multiple fabs, ensuring consistency, traceability, and alignment with global quality standards. Collaborating on the design, validation, and debugging of QA disposition rules to ensure best-in-class performance across the fab network. Demonstrating advanced statistical methods and data science tools to analyze electrical and physical characterization data, identify trends, and assess Quality and Reliability risks. Driving productivity improvements by automating QA disposition processes through software scripting and workflow optimization. Participating in global forums to identify systemic issues, propose innovative solutions, and enhance the overall capability of the quality network. Bachelor's degree in electrical, Electronics, Material Science, or a related field (master's preferred). Electrical and Physical Failure Analysis of semiconductors. Wafer fab manufacturing processes, functional probe, and parametric testing. Statistical data analysis (e.g., JMP, Python, R) and problem-solving in a high-volume manufacturing environment. Multi-fab disposition management and cross-site collaboration. Quality systems, methodologies, and tools (e.g., FMEA, SPC, 8D, CAPA). Strong communication skills in English (verbal and written). Ability to manage multiple priorities and take ownership of broad responsibilities in a dynamic environment.