As the Principal Operations Engineer, you will drive improvements in manufacturing performance by designing and implementing Multivariate Statistical Process Controls (MSPC) and automated Functional Assessments. Your work will center on identifying critical process variables, establishing responsive control systems, and linking component geometry to functional outcomes through physics-based modeling.
Key Responsibilities
- Determine essential process inputs that influence output performance and develop monitoring systems to detect capability shifts
- Establish correlations between physical part characteristics and functional behavior using statistical methods and physics emulation
- Lead in-depth investigations into quality and throughput challenges, including low First Pass Yield and high Cost of Poor Quality
- Analyze large-scale datasets using inductive and deductive techniques to uncover root causes and recommend corrective actions
- Design and deploy multivariate controls leveraging both standard SPC frameworks and custom analytical approaches
- Use tools such as PFMEAs and Characteristics Matrices to map process inputs to outputs
- Conduct Designed Experiments (DOE) and build statistical models to understand performance drivers
- Support continuous improvement initiatives by applying Lean principles and data-driven decision making
- Collaborate with cross-functional engineering teams and operations partners across module centers and suppliers
- Deliver technical insights to management and influence implementation of process enhancements
- Travel up to 25% to support on-site activities at manufacturing centers, supplier locations, and key business events
Qualifications
Applicants must hold a Bachelor’s degree in engineering or a related technical field with at least 8 years of experience, or a Master’s degree with 5 years of relevant experience in manufacturing or industrial operations. Proficiency in Microsoft Office, statistical software (Minitab, JMP, MATLAB), and data query tools is required. Experience in root cause analysis, corrective action implementation, and data warehouse systems is essential.
Preferred candidates will have Six Sigma Black Belt or Red-X Journeyman certification, experience presenting to leadership, and advanced skills in statistical modeling including Principal Component Analysis, Linear Regression, Partial Least Squares, and Gaussian Process Modeling. Familiarity with Databricks, SQL, and Python is strongly desired.
Work Environment
This is a remote position available to candidates in the United States, with periodic travel to manufacturing and supplier sites. The role supports a flexible schedule and aligns with a culture focused on innovation, mission-driven outcomes, and continuous learning.
