Senior Data Scientist -Smart Manufacturing

About the Role

We are looking for a Data Scientist to join a Smart Manufacturing and Artificial Intelligence team focused on building end-to-end digital and analytics solutions. This role will work at the intersection of advanced analytics, machine learning, and manufacturing to drive innovation and operational excellence.

The position will play a key role in developing digital twin capabilities for semiconductor manufacturing environments, enabling real-time insights, predictive analytics, and data-driven decision-making.

Key Responsibilities

Digital Twin Development

• Build and maintain data-driven models representing manufacturing processes, equipment behavior, and facility systems
• Integrate data from IoT sensors, manufacturing systems (MES), and facility platforms into digital twin environments

Predictive Analytics & Machine Learning

• Develop models for predictive maintenance, throughput optimization, and energy efficiency
• Apply statistical and machine learning techniques to forecast equipment failures, cycle times, and yield performance

Simulation & Optimization

• Run simulations and “what-if” scenarios for capacity planning, layout optimization, and process improvements
• Identify opportunities to improve productivity, reduce costs, and optimize energy usage

Data Integration & Visualization

• Design dashboards and visual tools to monitor key manufacturing KPIs (e.g., OEE, WIP, energy consumption)
• Support visualization of operations through advanced 3D or simulation environments

Collaboration

• Partner with cross-functional teams including engineering, facilities, planning, and IT
• Contribute to broader initiatives in AI, automation, and Industry 4.0

Key Requirements

• Strong programming skills in Python, R, and SQL
• Experience with machine learning frameworks such as TensorFlow or PyTorch
• Familiarity with data engineering concepts and large-scale data processing
• Understanding of semiconductor manufacturing processes or similar industrial environments
• Experience with IoT data pipelines, MES systems, and cloud platforms (e.g., AWS, Azure)
• Exposure to simulation or 3D visualization tools (e.g., Omniverse, Unity, or similar) is an advantage