At WD, our vision is to power global innovation and push the boundaries of technology to make what you thought was once impossible, possible.

At our core, WD is a company of problem solvers. People achieve extraordinary things given the right technology. For decades, we've been doing just that—our technology helped people put a man on the moon and capture the first-ever picture of a black hole.

We offer an expansive portfolio of technologies, HDDs, and platforms for business, creative professionals, and consumers alike under our Western Digital®, WD®, and WD_BLACK™.

We are a key partner to some of the largest and highest-growth organizations in the world. From enabling systems to make cities safer and more connected, to powering the data centers behind many of the world's biggest companies and hyperscale cloud providers, to meeting the massive and ever-growing data storage needs of the AI era, WD is fueling a brighter, smarter future.

Today's exceptional challenges require your unique skills. Together, we can build the future of data storage

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

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 [HIDDEN TEXT] 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 [HIDDEN TEXT].

Operation Analytic Engineer