Senior - Principal Engineer, Semiconductor Data Analytics

Join a global automotive technology company developing next-generation semiconductor solutions. You will be in charge of developing advanced semiconductor data analytics capabilities to understand silicon performance, yield, and product readiness for mass production.

Senior/ Principal Engineer, Semiconductor Data Analytics

Job Description

• Semiconductor Data Analytics - Develop advanced data analytics methods to understand chip performance, identify trends, investigate failures, and generate insights that support IC design and product improvements.

• Yield & Quality Improvement - Analyse semiconductor manufacturing and test data to understand yield losses, detect abnormal results, identify potential defects, and improve product quality andreliability.

• Data Screening & Performance Analysis - Develop statistical analysis and screening methods to identify outliers, detect manufacturing issues early, and reduce defective chips reaching customers. Experience with PAT, SBL, and clustering analysis is an advantage.

• Analytics Platforms & Data Infrastructure - Develop and manage data analytics platforms, databases, dashboards, and automated reporting solutions to help engineering teams access and analyze semiconductor data efficiently.

• Industrialization & Digitalisation - Work with Design, Product Engineering, Test Engineering, and manufacturing partners to turn data insights into actionable improvements, supporting new product qualification, production ramp-up, and mass production.

Job Requirements

• Degree or postgraduate qualification in Electrical/Electronics Engineering, Computer Science, Data Science, Statistics, or a related field, with at least 5 years of relevant experience for Senior Engineer or 8 years for Principal Engineer. Prior working experience in the semiconductor industry is essential.

• Semiconductor Data Analytics- Hands-on experience analyzing IC design, silicon performance, semiconductor test, yield, reliability, or manufacturing data to support product understanding and engineering decisions.

• Data Analytics Tools &Programming- Proficiency in tools such as Python, SQL, JMP, Power BI, Tableau, or Excel/VBA. Experience with Databricks or other semiconductor analytics platforms is an advantage.

• Statistical Analysis & Quality Screening- Knowledge of statistical analysis, SPC, outlier detection, PAT, SBL, or clustering techniques. Familiarity with semiconductor test data formats such as STDF/ATDF and ATE test operations is beneficial.

• Problem-Solving & Collaboration- Strong analytical and troubleshooting skills, with the ability to translate complex data into practical engineering insights. Able to work independently and collaborate with cross-functional teams to improve chip performance, yield and readiness for mass production.