Senior Wafer Process Analytics Engineer

At Seagate, our mission is to build the world's best data storage devices. The Advanced Transducer Development group is a global R&D organization developing new recording head technologies to advance areal density and support Seagate's HDD product roadmap. Our process team uses state-of-the-art wafer fabrication tools and cleanroom facilities to develop leading-edge writer and read-sensor technologies.

At Seagate, our mission is to build the world's best data storage devices. The Advanced Transducer Development group is a global R&D organization developing new recording head technologies to advance areal density and support Seagate's HDD product roadmap. Our process team uses state-of-the-art wafer fabrication tools and cleanroom facilities to develop leading-edge writer and read-sensor technologies.

Our team demonstrates the feasibility of new devices, process modules, materials, and integration concepts. If you enjoy wafer process development, hands-on fabrication, and solving complex device-build challenges, this could be an excellent opportunity to advance your R&D career.

• Develop advanced analyses to understand and improve critical dimension control, process variation, device geometry, and wafer-level performance.
• Build predictive and empirical models connecting process inputs, material properties, and metrology results to key process and device outputs.
• Develop, validate, and deploy AI and machine-learning solutions for process optimization, excursion identification, and downstream prediction.
• Create automated image-analysis workflows for SEM, optical, AFM, and other characterization data, including segmentation, feature extraction, dimensional measurement, and defect classification.
• Develop empirical unit-process models and wafer-process simulations that predict geometry evolution, process interactions, and cumulative variation through integrated device flows.
• Partner with unit process, integration, metrology, and design engineers to translate engineering problems into practical analytical solutions.
• Design experiments and data-collection strategies that produce reliable training data, isolate key process factors, and support model validation to connect downstream targeting to in-room para metrics.
• Deploy maintainable engineering tools, scripts, dashboards, or automated pipelines while establishing sound practices for data traceability, version control, and model documentation.
• Communicate model performance, uncertainty, limitations, and recommendations to technical and management audiences, and contribute to technology transfer and intellectual property.

• You are self-motivated, intellectually curious, and energized by solving complex engineering problems with data.
• You combine strong analytical skills with physical intuition and seek to understand the mechanisms behind statistical relationships.
• You can translate loosely defined technical challenges into structured analyses, models, experiments, and actionable recommendations.
• You collaborate effectively across engineering, science, software, and technician teams and communicate complex concepts clearly.
• You value data quality, reproducibility, model validation, and thorough documentation.
• You are comfortable working in a combination of cleanroom, laboratory, and office environments.

• Strong experience with Python, MATLAB, R, or similar tools for scientific computing, data analysis, model development, and workflow automation.
• Experience applying statistical methods such as regression, design of experiments, ANOVA, statistical process control, multivariate analysis, or process-capability analysis.
• Experience developing and validating machine-learning or AI…