Senior Engineer, Automation Development Engineering

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.

The Early Career Talent (ECT) - Data Analytics / Computer Science (Master's Level) will join Western Digital's Head Backend Business Unit under the BEST Program. This THO-based role carries elevated expectations reflecting graduate-level expertise in data science, machine learning, or advanced analytics applied to a precision manufacturing context.

The ECT will drive substantive analytics initiatives -- including predictive modeling, advanced process monitoring, and automation of data workflows -- within the slider processing and HGA assembly engineering environment at THO.

Reporting to the Head Backend Engineering organization, the ECT is expected to bring independent research capability and contribute to data infrastructure and intelligence projects that directly impact yield, quality, and operational efficiency.

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

REQUIRED

We warmly welcome fresh graduates of a Master's degree, and Early Career Talent with 0–2 years of experience are encouraged to apply.

Responsibilities

Developing predictive models and advanced statistical analyses for process yield and defect prediction; Building scalable data pipelines and analytics platforms for engineering use; Leading applied analytics projects in Phase 2 of the BEST Program with mentor oversight; Partnering with manufacturing and process engineers to embed data-driven methodologies into daily operations; Contributing to automation of data collection, reporting, and anomaly detection; Preparing and presenting technical findings to engineering leadership, including cross-site reviews

Requirements

Currently pursuing or recently completed a Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering with advanced analytics/AI specialization , or equivalent experience; Fresh graduate or maximum 2 years post-graduate experience; Advanced proficiency in Python, R, and/or SQL; Demonstrated experience in machine learning, predictive modeling, or advanced statistical analysis; Research or thesis experience with data-driven methodology; Advanced machine learning and statistical modeling (supervised, unsupervised, time-series); Strong Python/R programming with software engineering discipline (version control, documentation); Data architecture and pipeline development; Ability to translate complex models into operational recommendations; High analytical rigor and independent thinking

Nice to have

Thesis or published research in ML, AI, process analytics, or manufacturing intelligence; Experience with big data platforms, cloud analytics (AWS, Azure, GCP), or edge computing; Knowledge of time-series analysis, anomaly detection, or sensor data analytics; Familiarity with manufacturing execution systems (MES) or industrial IoT data; Experience building end-to-end ML pipelines (data ingestion to model deployment)