Yield & Data Analytics Engineer - Hybrid

Intel Corporation; Phoenix (AZ); Hybrid; USD 122,000 - 232,000; Full time

Benefits offered by this job

Stock bonuses

Health benefits

Retirement plan

Vacation

Job summary

Intel Corporation in the United States is seeking a Yield Product Development Engineer to advance process technologies within MDCE. You will identify root cause yield limiters and drive solutions to improve manufacturability and yield across technology nodes, contributing to high-volume manufacturing and robust product introductions.

Responsibilities include data consolidation, statistical analysis, ML-based insights, collaboration with design teams, and development of measurement recipes to

Qualifications

• Master's or Ph.D. in semiconductor science-related STEM field.
• 5+ years with Master's, or 2+ years with Ph.D. in advanced semiconductor devices and process flow.
• Experience with data analysis systems and software tools is preferred.

Responsibilities

• Develop methods and systems to analyze big data for defect mode understanding and yield modeling.
• Perform statistical analysis, create visualizations, and map process development roadmaps.
• Extract insights from structured and unstructured data using statistics, ML, and coding.
• Collaborate with design teams to resolve yield issues across process and product specs.
• Develop multivariate algorithms to identify root causes and suggest process changes.

Skills

Machine learning

Statistical analysis

Coding techniques

Data analysis

Cross-functional collaboration

Education

Master's degree in semiconductor science-related STEM field

Ph.D. in semiconductor science-related STEM field

Job description

Intel Corporation in the United States is seeking a Yield Product Development Engineer to advance process technologies within MDCE. You will identify root cause yield limiters and drive solutions to improve manufacturability and yield across technology nodes, contributing to high-volume manufacturing and robust product introductions.

Responsibilities include data consolidation, statistical analysis, ML-based insights, collaboration with design teams, and development of measurement recipes to