Operation Analytic Engineer

As the Manufacturing Engineer  with Data Science background to lead AI initiatives within the Substrate manufacturing environment. This is a hands-on individual contributor role that bridges process engineering and advanced analytics translating manufacturing data into actionable intelligence and deploying AI solutions that directly improve yield quality and operational efficiency across Plating Washing and Polishing processes.

ESSENTIAL DUTIES AND RESPONSIBILITIES:

• Focal person for plating facilities related issues ie plating DI water trend monitoring LPC and  
• contamination 
• Focal person of plating chemical planning and inventory  
• Focal person for plating pretreatment strip line filter change and descaling.  
• Monitor and control plating processes to ensure quality and yield targets
• Implement process improvements and optimization initiatives
• Troubleshoot process deviations and implement corrective actions which include thickness control anodic protection
• Troubleshoot line to line variation and implement corrective actions
• Ensure compliance with contamination control procedures
• Participating in FMEA activities and risk assessments
• Monitor and improve plating OEE performance
• Implement process optimizations to reduce speed losses and improve efficiency
• Track and analyze OEE metrics to identify improvement opportunities
• Support implementation of process parameter revisions to enhance OEE
• Identify define and document KPIVs and KPOVs for plating processes and establish data-driven linkages between them
• Develop and maintain process monitoring dashboards using data analytics tools to visualize KPIV-KPOV relationships
• Apply machine learning models (e.g. regression classification anomaly detection) to predict process outcomes and enable proactive process control
• Collaborate with data engineers or IT teams to integrate manufacturing data from equipment and sensors into analytics platforms
• Utilize statistical process control (SPC) and advanced analytics to detect processes that drift and trigger timely corrective actions

Qualifications :

REQUIRED:

• Bachelors or Masters degree in Data Science Computer Science Electrical Engineering Materials Engineering Chemical Engineering or a closely related field.
•  At least 2 years in a manufacturing or process engineering environment and demonstrable data science or analytics project ownership. 

PREFERRED:

• Must be able to use computer for communication (email / Microsoft Office applications & etc). 
• Able to communicate in English. 

• Exposure to data analytics tools such as Python R JMP Minitab Power BI or Tableau
• Basic understanding of machine learning concepts and their application in manufacturing process control
• Experience or academic exposure to KPIV/KPOV identification and correlation analysis

SKILLS

• Hands-on experience building and deploying machine learning models (classification regression clustering anomaly detection) in a production or near-production context.
• Experience working with manufacturing data systems such as MES ERP or SCADA/IoT sensor platforms.
• Visualization: Power BI Spotfire or equivalent BI tools for operational dashboards.
• Statistical Methods: SPC DOE Cpk analysis hypothesis testing regression multivariate analysis.
• AI Tools: Practical experience with generative AI tools such as Microsoft 365 Copilot.

Additional Information :

#LI-SW1

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Remote Work :

No

Employment Type :

Full-time