Manufacturing Engineer – Data Science at Western Digital

Pasir Gudang, Johor, Malaysia -

Full Time

Start Date

Immediate

Expiry Date

17 Sep, 26

Salary

0.0

Posted On

19 Jun, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Python, Statistical Modeling, SQL, Data Visualization, Time-series Analysis, Anomaly Detection, Predictive Maintenance, MLOps, Cloud Platforms, SPC, DOE, Cpk Analysis, Multivariate Analysis, Spotfire, Industrial IoT

Industry

Computer Hardware Manufacturing

Description

Company Description 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 ESSENTIAL DUTIES AND RESPONSIBILITIES: Identify, develop, and deploy AI use cases within AlMg Substrate manufacturing, targeting yield improvement, defect reduction, and equipment downtime minimization across Plate, Wash, and Polish processes. Build predictive and prescriptive models using available internal AI tools on manufacturing process data extracted from MES, ERP, and IoT/sensor sources. Apply statistical analysis techniques including SPC, DOE, Cpk analysis, hypothesis testing, and multivariate analysis to uncover KPIV-KPOV relationships in substrate manufacturing processes. Develop and maintain end-to-end(Substrate-Media-HDD) analytics pipelines - from data extraction and preparation through model training, validation, and production deployment. Create self-serve data dashboards and automated reports using Spotfire to support real-time Cpk, Yield, and SPC monitoring for the manufacturing line. Support AI-driven root cause analysis for quality excursions, reducing manual investigation cycle time. Provide engineering support for Substrate manufacturing process issues, applying data science tools to accelerate root cause identification and corrective action deployment. Collaborate cross-functionally with Process, Quality, Test/FA, and Metrology teams to integrate analytics capabilities into existing engineering workflows. Provide engineering support for Substrate manufacturing process issues, applying data science tools to accelerate root cause identification and corrective action deployment. This position is part of our Early Career program at WD. Our Early Career program is designed to support individuals beginning their professional career by providing the foundational training through a structured onboarding, mentorship, and development curriculum. Qualifications Qualifications REQUIRED: Master's degree in Data Science, Computer Science, Artificial Intelligence, Electrical Engineering or a closely related field. PREFERRED: Strong analytical and problem-solving skills. Good communication and teamwork abilities. Prior involvement in AI/ML pilot projects or smart manufacturing initiatives. SKILLS Strong foundation in machine learning algorithms (supervised, unsupervised, and reinforcement learning) and statistical modeling. Proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow or PyTorch) for data analysis and model development. Experience with data visualization tools (Matplotlib, Seaborn, Plotly, Power BI, or Tableau). Familiarity with SQL and database querying for structured data extraction. Knowledge of time-series analysis, anomaly detection, or predictive maintenance modeling is a strong advantage. Exposure to manufacturing process data, sensor data, or industrial IoT (IIoT) environments is preferred. Understanding of MLOps practices (model versioning, CI/CD for ML pipelines) is an added advantage. Familiarity with cloud platforms (Azure, AWS, or GCP) for ML workloads is a plus. 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 [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: Data Science Work Location: Pasir Gudang WD Media Office--LOC_WDT_MY0101

Responsibilities

Develop and deploy AI use cases to improve yield and reduce defects in substrate manufacturing processes. Build predictive models and analytics pipelines using data from MES, ERP, and IoT sources to support real-time monitoring and root cause analysis.