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Tier-1 brand and remote plus metro presence, but highly specialized semiconductor manufacturing requirements limit competition.
Requires deep semiconductor manufacturing quality, GD&T, SPC and supplier experience, making cross-industry transfer difficult.
Explicit 10-year minimum plus deep manufacturing quality, statistical, and AI/tool requirements make filtering stringent.
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Define and codify manufacturing and quality business rules by analyzing engineering drawings, GD&T, supplier/manufacturing data, and critical quality characteristics.
Translate engineering and quality domain knowledge into clear, testable requirements for IT, software developers, and data teams to implement AI-enabled manufacturing solutions.
Lead AI-enabled process transformation initiatives end-to-end, including opportunity identification, business case development, requirement definition, validation, and implementation.
Bachelor’s or master’s degree in Mechanical, Manufacturing, Industrial Engineering, Data Science, AI, or related field.
Minimum 10 years experience with bachelor’s degree, or minimum 6 years with master’s degree.
Proven experience in manufacturing, supplier quality, process engineering, or digital transformation roles.
Strong knowledge of engineering drawings, GD&T, SPC, DOE, Gage R&R, quality systems, and proficiency in data analysis tools (Python, SQL, Minitab, JMP).
Experienced in leading cross-functional projects at the intersection of engineering, data, and software delivery within manufacturing environments.
Demonstrated ability to apply advanced quality/statistical engineering methods and translate complex domain knowledge into technical solutions.
Background in AI/ML and automation applied to manufacturing, with a track record of managing AI-driven process improvements end-to-end.