Senior AI Data Scientist - Manufacturing Analytics

COHERENT; Fremont (CA); On-site; USD 118,000 - 202,000; Full time

Job summary

Coherent Corp. in Fremont, CA, seeks a data scientist/ML engineer to analyze datasets from wafer fabrication and related systems, applying AI, ML, and statistics to deepen understanding of devices and processes.

You will build reusable data pipelines, dashboards, and model-monitoring methods, collaborating with engineers to translate findings into actionable recommendations and informing decision-making across teams. On-site initially, with potential hybrid work later.

Qualifications

• Minimum 5 years of experience applying AI/ML to real-world engineering or manufacturing problems.
• Bachelor's degree in Data Science, Engineering, Science, CS or Mathematics; Master's preferred.
• Strong SQL and data-processing experience.
• Experience deploying AI/ML models via REST APIs and containers.

Responsibilities

• Analyze datasets from wafer fabrication processes, equipment, sensors, metrology systems, and MES.
• Apply statistical analysis, AI, ML, and data-mining techniques for deeper understanding of devices and processes.
• Develop reusable data pipelines, analytical tools, dashboards, and model-monitoring methods.
• Translate analytical results into clear engineering insights and actionable recommendations.
• Communicate findings to engineers and managers; distinguish correlation from causation.
• Support design of experiments, process characterization, and continuous improvement.

Skills

Machine learning

Statistical analysis

Python

SQL

Data pipelines

Model deployment

Cloud ML (AWS/GCP/Azure)

Education

Bachelor's degree in Data Science, Engineering, Science, CS or Mathematics

Master's degree preferred

Tools

Python libraries (NumPy, pandas)

Docker

Kubernetes

REST APIs

Databricks

AWS / GCP / Azure

Job description

Coherent Corp. in Fremont, CA, seeks a data scientist/ML engineer to analyze datasets from wafer fabrication and related systems, applying AI, ML, and statistics to deepen understanding of devices and processes.

You will build reusable data pipelines, dashboards, and model-monitoring methods, collaborating with engineers to translate findings into actionable recommendations and informing decision-making across teams. On-site initially, with potential hybrid work later.