Additionalinformation

Additional Information

Renesas is an embedded semiconductor solution provider driven by its Purpose, To Make Our Lives Easier. With a global team of over 21,000 engineers and problem solvers in more than 30 countries, we offer the opportunity to work on world‑leading technology for Automotive, Industrial, Infrastructure, and IoT, shaping a safer, healthier, greener, and smarter future.

At Renesas, TAGIE is our culture, grounded in being Transparent, Agile, Global, Innovative, and Entrepreneurial. It shapes how we work, grow and deliver on our purpose together. This collaborative spirit and mindset drive our semiconductor technology to transform industries and impact millions of lives.

We believe in rewarding our employees with a competitive benefits package alongside their salary. More information will be provided during the hiring process.

Are you ready to join our team and shape the future with us?

Renesas Electronics is an equal opportunity and affirmative action employer, committed to supporting diversity and fostering a work environment free of discrimination on the basis of sex, race, religion, national origin, gender, gender identity, gender expression, age, sexual orientation, military status, veteran status, or any other basis protected by law. For more information, please read our Diversity & Inclusion Statement.

Companydescription

Company Description

Jobdescription

Job Description

Key Responsibilities

• 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 introduction (NPI) and process changes.
• Partner with Equipment/Maintenance and Quality teams to troubleshoot equipment-related process issues.
• Document standard work, process specifications, and lessons learned to support knowledge transfer and training.
• Present project results and improvement recommendations to the Process Engineering Manager and cross-functional stakeholders.

Qualifications

Required Qualifications

• 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).
• Comfortable working directly on the shop floor with equipment, operators, and production data.