Senior Wafer Process Analytics Engineer

About our group:

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.

About the role - you will:

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.

About you:

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.

Your experience includes:

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 models using engineering, manufacturing, scientific, or image-based data.

Experience with image processing or computer vision, including segmentation, feature extraction, classification, object detection, dimensional measurement, or image registration.

Experience building process-accurate models and calibrated unit process simulations, using empirical and/or physics informed models on commercial platforms or home-built code.

Ability to organize and analyze complex datasets from multiple sources and communicate results through clear visualizations and technical presentations.

Bachelor’s degree in Data Science, Computer Science, Electrical Engineering, Chemical Engineering, Materials Science, Mechanical Engineering, Physics, Applied Mathematics, Statistics, or a related technical field with 5+ years of relevant experience; a Master’s degree with 3+ years; a PhD with 0+ years; or equivalent education and experience.

You might also have

Experience with semiconductor fabrication, nanotechnology, MEMS, magnetic devices, photonic devices, or advanced manufacturing.

Familiarity with lithography, thin-film deposition, plasma or wet etch, CMP, cleans, thermal…