Manufacturing Yield & Device Analytics Engineer

Noveon International•San Marcos, TX

2d•Onsite

About The Position

You will be responsible for tracking, optimizing, analyzing, and sustaining manufacturing performance to directly improve operational efficiency, product quality, yield performance, and cost-effectiveness. This role completely bridges physical manufacturing fundamentals with advanced analytical problem-solving, deep process capability analysis, and massive data-driven decision-making. The ideal candidate behaves like an analytical sleuth: leveraging manufacturing data trends, statistical analysis, and process validation to drive yield optimization, eliminate waste, and deliver concrete process capability statistics back to design engineering to establish realistic manufacturing design rules.

Requirements

Bachelor’s degree in Mechanical Engineering, Manufacturing Engineering, Industrial Engineering, Electrical Engineering, Materials Science, or a highly related technical field.
3–7+ years of experience in high-precision manufacturing, process engineering, device engineering, or yield analysis environments.
Exceptional analytical, statistical modeling, and data interpretation skills.
Deep hands-on experience utilizing SPC, process capability analysis, complex DOE, and statistical problem-solving methodologies.
Highly proficient in rigorous root cause analysis and corrective/preventive action implementation.
Strong working knowledge of manufacturing systems, production optimization, KPI tracking (yield, scrap, OEE, throughput), and Lean/Six Sigma principles.

Nice To Haves

Prior experience as a Device Engineer, Yield Engineer, or Yield Analytics Specialist within a semiconductor fabrication or advanced precision electronics environment is highly advantageous.
Advanced Excel skills, with strong experience utilizing Minitab, Power BI, SQL, JMP, or equivalent manufacturing data analysis software.
Experience supporting automated equipment, manufacturing digitization, or Industry 4.0 initiatives.
Lean Six Sigma Green Belt or Black Belt certification is a major asset.
Familiarity with APQP, PPAP, FMEA, and Control Plan methodologies.

Responsibilities

Drive Yield Improvement: Utilize data analytics and statistical methodologies to proactively identify process variation, pinpoint production inefficiencies, and execute targeted yield improvement and waste reduction opportunities.
Statistical Performance Tracking: Analyze high-volume manufacturing data trends, SPC metrics, scrap rates, equipment downtime, cycle times, and overall yield performance to spearhead continuous improvement initiatives.
Lead Advanced DOE: Direct complex Design of Experiments (DOE) activities to establish robust process windows, stabilize process robustness, and achieve challenging process capability targets.
Predictive Performance Modeling: Develop predictive and preventive improvement strategies through analytical modeling and process performance evaluation.
Define Manufacturing Design Rules: Deliver precise process capability numbers and statistical performance metrics to Design Engineering to help define, refine, and enforce realistic design rules for optimal manufacturing scaling.
Cross-Functional Collaboration: Collaborate cross-functionally with Production, Quality, Supply Chain, and Operations teams to instill a culture of rigorous, data-based decision-making across the organization.
Root Cause Diagnostics: Apply advanced root cause analysis methodologies (including 8D, 5 Whys, Fishbone, Pareto Analysis, and full CAPA implementation) to diagnose sudden process disruptions or quality events.
Quality & Regulatory Compliance: Ensure all yield-enhancement initiatives and process changes conform to company Quality Management Systems (QMS), supporting ongoing compliance with ISO 9001 and automotive standards.
Support Digitization & Industry 4.0: Support manufacturing digitization, automation, and smart factory initiatives to improve process consistency and unlock deep operational visibility.
Equipment Capacity Evaluation: Evaluate manufacturing equipment performance, capacity limitations, tool utilization, and baseline process efficiency.
Product Cost Analytics: Conduct thorough product cost analyses including labor touch-time, machine utilization, scrap generation, cycle time, and process-related cost drivers.
Documentation & Risk Assessment: Update and maintain critical Process FMEAs (PFMEAs), Control Plans, and process risk assessments based on ongoing yield data analysis.