Western Digital Hiring! Full Time Operation Analytic Engineer in Johor - Ricebowl

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

REQUIRED:

• Bachelor's or Master's 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.