Renesas is one of the top global semiconductor companies in the world. We strive to develop a safer, healthier, greener, and smarter world, and our goal is to make every endpoint intelligent by offering product solutions in the automotive, industrial,...
About the role
• Analyze production data to identify yield, cycle time, and cost improvement opportunities on assigned manufacturing lines. • Plan and execute Lean, Six Sigma, and TPM projects — from DOE design through implementation and results validation — as part of the site’s Operational Excellence roadmap.
What they're looking for
- Bachelor’s degree in Industrial, Manufacturing, Electrical, Mechanical, or SW Engineering, Computer Science, or a related technical field
- 2–5 years of experience in manufacturing, process, or industrial engineering, ideally in semiconductor or electronics manufacturing
- Hands-on experience with Lean Six Sigma tools (DOE, root-cause analysis, value stream mapping), Green Belt or working equivalent preferred
- Practical experience building or deploying machine learning models (e.g., classification, regression, computer vision) using Python and common ML libraries
- Working knowledge of statistical process control (SPC), process capability (Cpk) analysis, and data visualization tools
- Strong data analysis skills (Python, SQL, Minitab, JMP, or Excel/Power BI at minimum)
More about this role
• Analyze production data to identify yield, cycle time, and cost improvement opportunities on assigned manufacturing lines.
• Plan and execute Lean, Six Sigma, and TPM projects — from DOE design through implementation and results validation — as part of the site’s Operational Excellence roadmap.
• Build and deploy machine learning models for manufacturing use cases — e.g., yield prediction, computer-vision defect detection, and anomaly/excursion detection — in partnership with Data Science/IT.
• Develop real-time dashboards, automated reports, and data pipelines that give engineers and operators faster visibility into process performance.
• Support automation and Industry 4.0 initiatives — MES integration, IIoT sensor deployment, and robotics/automated material handling — from pilot through production rollout.
• Maintain and improve statistical process control (SPC) systems, monitoring process capability (Cpk) and flagging out-of-control conditions.
• Investigate process-related yield and quality excursions using structured root-cause methods (8D, DMAIC, FMEA) and implement corrective/preventive actions.
• Support process qualification and control plans for new product...
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