Principal AI Data Scientist – Wafer Fabrication

Job Description

• Analyze datasets generated by wafer fabrication processes, equipment, sensors, metrology systems, and manufacturing execution systems.
• Apply statistical analysis, AI, machine learning, and data-mining techniques for:
• Deeper understanding of devices and fabrication processes.
• Process monitoring and anomaly detection
• Wafer and lot excursion analysis
• Yield analysis and prediction
• Collaborate with others using AI & ML
• Integrate data from multiple sources, including process recipes, equipment logs, sensor data, metrology results, defect inspection, and production history.
• Translate analytical results into clear engineering insights and actionable recommendations.
• Communicate these findings to engineers and managers.
• Work with engineers to distinguish correlation from likely physical or process-driven causation.
• Develop reusable data pipelines, analytical tools, dashboards, and model-monitoring methods.
• Support design of experiments, process characterization, and continuous improvement activities.
• Help establish best practices for data quality, feature engineering, model validation, documentation, and responsible use of AI in development and manufacturing.

• Analyze datasets generated by wafer fabrication processes, equipment, sensors, metrology systems, and manufacturing execution systems.
• Apply statistical analysis, AI, machine learning, and data-mining techniques for:
• Deeper understanding of devices and fabrication processes.
• Process monitoring and anomaly detection
• Wafer and lot excursion analysis
• Yield analysis and prediction
• Root-cause investigation
• Collaborate with others using AI & ML
• Integrate data from multiple sources, including process recipes, equipment logs, sensor data, metrology results, defect inspection, and production history.
• Translate analytical results into clear engineering insights and actionable recommendations
• Communicate these findings to engineers and managers.
• Work with engineers to distinguish correlation from likely physical or process-driven causation.
• Develop reusable data pipelines, analytical tools, dashboards, and model-monitoring methods.
• Support design of experiments, process characterization, and continuous improvement activities.
• Help establish best practices for data quality, feature engineering, model validation, documentation, and responsible use of AI in development and manufacturing.

• The Candidate will have a minimum of 5 years of experience with AI, preferably including ML, preferably with experience in statistical analytic techniques.
• A Bachelor’s degree in Data Science, Engineering, Science, Computer Science or Mathematics is required, a Master’s degree is preferred.

The candidate will have competency in several of the following areas:

• Several years of professional experience applying machine learning, artificial intelligence, advanced statistics, and data science to real-world engineering or manufacturing problems.
• Experience with SQL and modern data-processing or data-platform technologies.
• Strong knowledge of statistical methods, experimental design, regression, classification, clustering, anomaly detection, and model evaluation.
• Programming skills in Python and experience with common data-science and machine-learning libraries.
• Experience in AI/ML model deployment through RESTful APIs, containerization, and container orchestration.
• Experience with cloud-based ML training and inference using AWS, GCP, Azure, or Databricks is a plus.
• Experience working with structured, time-series, sensor, or high-volume manufacturing data.
• Exposure to wafer fabrication, photonics, and telecommunications is a plus.
• Ability to work collaboratively with domain experts and explain complex analytical results in practical engineering terms.
• Demonstrated ability to take an analysis from problem definition through deployment, validation, and communication of results.

The job is on-site for the first three months, with the possibility to convert to hybrid afterwards. The job requires weekly morning hours and occasional evening and weekend hours. Travel to other Coherent sites in the Bay Area and the US may be possible to share knowledge with other experts.

• Sitting or standing several hours per day, with the option of having a sit-stand desk.
• Extensive keyboard and mouse work.

All employees are required to follow the site EHS procedures and Coherent Corp. Corporate EHS standards.