Testing Engineering, Manufacturing (AI)

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.

Responsibilities

Contribute to factory operation data analysis and implementation of intelligent solutions, including quality control and energy consumption, to identify and resolve business issues.; Use SQL, Python, and other tools for data extraction, cleaning, and analysis, and provide actionable insights via visualization tools such as Spotfire or Power BI.; Independently research and apply advanced algorithmic models (e.g., time-series forecasting, anomaly detection), evaluate their business value, and drive implementation in manufacturing scenarios.; Participate in AI application projects for manufacturing, including quality inspection, process optimization, and predictive maintenance, and support pilot rollouts.; Collaborate with cross-functional teams to analyze business requirements and propose data-driven improvement solutions.; Apply NLP and machine learning methods for intelligent classification, root cause analysis, and model optimization.; Prepare technical documentation and project reports to support digital transformation and intelligent manufacturing strategies.

Requirements

Master’s degree or above in Data Science, Computer Science, Statistics, Industrial Engineering, Automation, Mechanical Engineering, or related fields.; Proficiency in at least one data analysis language (Python or R) and familiarity with data science libraries (e.g., Pandas, NumPy, Scikit-learn).; Strong skills in data processing and SQL.; Understanding and ability to apply machine learning and AI concepts (e.g., supervised/unsupervised learning, deep learning).; Strong analytical and problem-solving skills with logical thinking ability.; Excellent communication skills and ability to work effectively in multidisciplinary and cross-cultural teams.; Proactive, motivated, and willing to drive practical applications of data and algorithms in real industrial settings.