Role Description​

We are looking for a highly analytical, multidisciplinary student to join our team in a dynamic semiconductor manufacturing (FAB) environment. This role sits at the intersection ofhigh-volume manufacturing data science, and automation. You will gain hands-on experience with day-to-day FAB sustaining operations while playing an active role inmodernizing our workflows through smart automation and AI-driven solutions.​

If you are fascinated by the physical reality of hardware manufacturing and want to apply your coding and analytical skills to solve complex, real-world engineering problems, thisis the role for you.​

Key Responsibilities​

FAB Sustaining Operations: Support day-to-day manufacturing tasks, monitor equipment/process health, and investigate excursions to ensure continuous, high-quality output.​

Supports equipment troubleshooting, maintenance, and qualification activities. Collaborate with engineering teams to optimize process control methodologies and ensureproduction targets are met.​

Smart Automation: Identify manual bottlenecks in our reporting and engineering workflows and develop robust scripts to automate them.​

AI & Data Integration: Build and implement AI-driven solutions to detect correlations and prepare data for advanced analysis, moving our team toward predictive methodologies.​

Multidisciplinary Problem Solving: Bridge the gap between physical manufacturing processes and software/data engineering, learning the physics and mechanics behind the datayou analyze.​

Required skills

• Bsc, pursuing a degree in Physics, Physics  and Computer Engineering, Physics and Mathematics, Industrial Engineering with a specialization in Information Systems, or a related technical field. ​

• Minimum 4 semesters until graduation- required. ​

• Ability to work at least 20 hours per week. (2.5 days a week) ​

• Programming Skills: Strong proficiency in Python for data manipulation and automation.​

• Data Fluency: Exceptional analytical skills with the ability to query, manipulate, and extract actionable insights from massive, complex manufacturing databases (e.g., SQL).​

• Adaptability: A proven ability to step outside your core academic scope. You must be willing to learn the terminology, physics, and constraints of semiconductor manufacturing.​

• Innovative Mindset: A self-starter who proactively looks for opportunities to optimize legacy processes with modern tech.​

Preferred Skills​

• Experience with machine learning libraries (e.g., Pandas, Scikit-learn) and data visualization tools.​

• An understanding of basic manufacturing principles, process control, or hardware architecture.​

• Ability to work effectively in a fast-paced, multidisciplinary team environment.​

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