Senior AI Data Scientist — Wafer Manufacturing

Coherent Corp.; Fremont (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Coherent Corp. is seeking an experienced Data Scientist to analyze large manufacturing datasets from wafer fabrication, sensors, and MES.

You will apply AI/ML techniques to deepen understanding of devices and processes, monitor production, and predict yields, while collaborating with engineers to turn insights into actionable improvements. The role emphasizes building reusable data pipelines, dashboards, and model-monitoring solutions, with involvement in experiments and process

Qualifications

• Experience applying ML/AI to real-world engineering or manufacturing problems.
• Strong background in statistics, experimental design, regression, classification and anomaly detection.
• Proficiency in Python and common data-science libraries; SQL proficiency required.

Responsibilities

• Analyze datasets from wafer fabrication, equipment, sensors, metrology, and MES.
• Apply statistical analysis, AI, ML, and data-mining to improve devices and processes.
• Develop reusable data pipelines, dashboards, and model-monitoring methods.
• Translate results into engineering insights and actionable recommendations.
• Collaborate with engineers; distinguish correlation from causal factors.
• Support design of experiments and continuous improvement activities.

Skills

AI / ML

Statistical Analysis

Data Mining

Python

SQL

Time Series

REST APIs

Cloud ML

Experiment Design

Communication

Education

Bachelor’s in Data Science, Engineering, CS or Mathematics

Master’s preferred

Tools

Docker

Kubernetes

Pandas

Scikit-learn

MLflow

Job description

Coherent Corp. is seeking an experienced Data Scientist to analyze large manufacturing datasets from wafer fabrication, sensors, and MES.

You will apply AI/ML techniques to deepen understanding of devices and processes, monitor production, and predict yields, while collaborating with engineers to turn insights into actionable improvements. The role emphasizes building reusable data pipelines, dashboards, and model-monitoring solutions, with involvement in experiments and process