About the role
AI summarisedJoin the Data & Automation team to develop and deploy data-driven solutions using machine learning, statistical modeling, and AI techniques. You will solve complex challenges across fab operations—from wafer processing to predictive maintenance—by transforming high-volume raw fab data into actionable intelligence.
ResearchOnsiteInstitute of Microelectronics
Key Responsibilities
- Design, develop, and implement scalable data pipelines to ingest, clean, and structure high-volume, high-velocity data from fab tools.
- Apply advanced analytics, machine learning, and AI techniques (e.g., computer vision, time-series forecasting) to improve manufacturing outcomes.
- Build predictive and prescriptive models for yield prediction, equipment health monitoring, real-time process control, and defect classification.
- Collaborate with cross-functional teams to translate business problems into analytical frameworks and measurable KPIs.
- Deploy and monitor ML models in production environments, ensuring reliability and compliance with fab data governance standards.
- Stay current with emerging AI/ML technologies and assess their applicability to semiconductor manufacturing challenges.
Requirements
- Bachelor's degree in Data Science, Computer Science, Electrical Engineering, Industrial Engineering, Applied Mathematics, or related field.
- 2+ years of experience applying data analytics and/or machine learning in semiconductor manufacturing or fab automation.
- Strong programming skills in Python (pandas, scikit-learn, TensorFlow/PyTorch) and SQL.
- Knowledge of semiconductor processes (e.g., lithography, etch, deposition) or equipment data standards (SECS/GEM, GEM300).
- Experience working with time-series data, sensor data, or structured/unstructured manufacturing data.
- Familiarity with cloud platforms (AWS, Azure, or GCP) and big data tools (e.g., Spark, Kafka).
- Understanding of statistical methods, experimental design, and model validation techniques.