Senior Engineer, Manufacturing Engineering at Western Digital

Pasir Gudang, Johor, Malaysia -

Full Time

Start Date

Immediate

Expiry Date

04 Oct, 26

Salary

0.0

Posted On

06 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Analytics, Statistical Process Control, Python, R, Power BI, Tableau, JMP, Minitab, DOE, Cpk Analysis, Regression, Anomaly Detection, MES, SCADA, Generative AI

Industry

Computer Hardware Manufacturing

Description

Company Description WD is building the infrastructure behind the AI-driven data economy. As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time. That’s where we come in. We combine deep engineering expertise with global-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide. This isn’t theoretical work. It’s real systems, at real scale, people solving some of the hardest challenges in technology today. We’re looking for people who want to build, solve, and operate at that level. Join us and let’s shape the future of data. Job Description 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: 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. Additional Information #LI-SW1 WD thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution. WD is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [email protected] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying. Notice To Candidates: Please be aware that WD and its subsidiaries will never request payment as a condition for applying for a position or receiving an offer of employment. Should you encounter any such requests, please report it immediately to WD Ethics Helpline or email [email protected]. Job Type (exemption status): Exempt position - Please see related compensation & benefits details below Business Function: Manufacturing Engineering Work Location: Pasir Gudang WD Media Office--LOC_WDT_MY0101

Responsibilities

Lead AI initiatives within the substrate manufacturing environment to improve yield, quality, and operational efficiency. This involves monitoring plating processes, implementing process optimizations, and developing data-driven dashboards to link KPIVs and KPOVs.