Manufacturing Engineer – Data Science

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.

ESSENTIAL DUTIES AND RESPONSIBILITIES

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.

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.

Requirements

Master's degree in Data Science, Computer Science, Artificial Intelligence, Electrical Engineering or a closely related field.; 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.

Nice to have

Strong analytical and problem-solving skills.; Good communication and teamwork abilities.; Prior involvement in AI/ML pilot projects or smart manufacturing initiatives.