AI/ML Analytics Engineer – Digital Manufacturing

GlobalFoundries inc.; Town of Malta (NY); On-site; USD 58,000 - 101,000; Full time

Job summary

GlobalFoundries is seeking a new college graduate to join the Analytics Center of Excellence, Digital Manufacturing, to develop data‑driven solutions for semiconductor manufacturing.

Working with the Analytics Center of Excellence Digital Manufacturing Manager and Fab teams, you will define project objectives, prepare datasets, apply statistical and machine learning methods, and communicate actionable recommendations to stakeholders and Fab leadership.

Qualifications

• Graduating with Bachelor’s or Master’s in Data Science, Computer Science, Statistics, IE, EE, CE or related field.
• Minimum 3.0 GPA and good academic standing.
• Fluent in English (written and verbal).

Responsibilities

• Define project objectives, deliverables, success measures and timelines; support analytics methods development across multiple use cases.
• Collaborate with manufacturing, engineering and cross‑functional teams to translate requirements into data‑driven approaches.
• Collect, clean, integrate and preprocess large datasets; apply statistics, visualization and ML to identify patterns and opportunities.
• Develop, evaluate and implement predictive models and agentic solutions; communicate findings to stakeholders and Fab leadership.

Skills

Python

R

SQL

Statistical analysis

Machine learning

Communication

Leadership

Project management

Agentic solutions

Education

Bachelor's or Master's in Data Science, Computer Science, Statistics, Industrial Engineering, Electrical Engineering, Computer Engineering or related field

Tools

Python

R

SQL

Job description

GlobalFoundries is seeking a new college graduate to join the Analytics Center of Excellence, Digital Manufacturing, to develop data‑driven solutions for semiconductor manufacturing.

Working with the Analytics Center of Excellence Digital Manufacturing Manager and Fab teams, you will define project objectives, prepare datasets, apply statistical and machine learning methods, and communicate actionable recommendations to stakeholders and Fab leadership.