Product Engineering Analyst (Yield & Data)

Role Overview

• Drive data‑driven insights across semiconductor test structures and product lifecycles.
• Strong focus on yield improvement, failure analysis and product performance.
• Transform large‑scale manufacturing and test data into actionable insights.
• Collaborate at the intersection of Yield Engineering, Product Engineering and Data Science.
• Improve yield, quality and cost through advanced analytics and automation.
• Apply strong semiconductor domain expertise combined with data analysis capabilities.

Must Have

• Strong experience in data analysis within semiconductor or manufacturing environments
• Proficiency in Python (Pandas, NumPy, SciPy, Matplotlib)
• Strong knowledge of statistics and data modeling
• Understanding of semiconductor yield concepts and test flows (CP/FT)

Responsibilities

• Analyze large‑scale datasets from wafer sort (CP), final test (FT) and fab processes to drive yield and product insights.
• Conduct Pareto analyses, binning studies and correlation analyses across process, test and product parameters.
• Identify systematic yield limiters and lead root‑cause investigations of product and process failures.
• Develop and apply statistical and data‑driven models to support yield and performance optimization.
• Design, develop and maintain automated data pipelines and analysis tools.
• Create dashboards, automated reports and visualization tools for scalable insights.
• Enable data‑driven decision‑making across cross‑functional teams and stakeholders.

Your profile

• Basic programming proficiency, particularly in Python.
• Experience with SQL and database querying.
• Strong analytical mindset with a high level of attention to detail.
• Ability to translate complex data into meaningful engineering insights and actionable recommendations.
• Proactive, automation‑driven approach to problem solving.
• Comfortable working in cross‑functional, collaborative environments.

Ideal fit

• Background in Electrical Engineering or Microelectronics.
• Expertise as a Product Engineer and/or Yield Engineer.
• Familiarity with semiconductor test data formats (STDF/ATDF), including wafer maps and binning analysis.
• Experience using data visualization and analytics tools to interpret large datasets.
• Working knowledge of Design of Experiments (DOE) and Statistical Process Control (SPC) methodologies
• Proficiency working with large‑scale data pipelines and/or cloud‑based data platforms.