Senior Yield Analytics Engineer (Semiconductors)

Samsung Electronics Perú; Taylor (TX); On-site; USD 106,000 - 175,000; Full time

Benefits offered by this job

Medical benefits

Life insurance & 401(k) matching

Onsite facilities

Parental leave

Paid time off

Wellness incentives

Bonus program

Job summary

Samsung Austin Semiconductor is seeking a Yield Analysis & Improvement Leader to own end-to-end yield performance across wafer sort, parametric, and final test. You will apply data mining, statistics, and machine learning to identify yield limiters, and build automated analytics to correlate defects with inline data.

The role partners with Process Integration, Module, and Design teams to optimize process windows, reduce variability, and drive root cause investigations with FA teams.

Qualifications

• Basic qualifications: degree in Electrical Engineering, Materials Science, Physics, Applied Physics, or related field.
• 5+ years of experience in semiconductor yield or process integration preferred.
• Foundry background at a leading-edge fab (FinFET/GAA).
• Strong understanding of semiconductor device physics and wafer processing.
• Coding skills for data manipulation (Python, SQL).
• Proficiency in statistical analysis software (Spotfire, JMP) and Python libraries.

Responsibilities

• Own end-to-end yield performance across wafer sort, parametric test, and final test.
• Conduct advanced data mining and statistical analysis to identify yield limiters.
• Develop automated analytics pipelines (Python, JMP, SQL).
• Collaborate with Process Integration, Module, and Design teams to optimize windows.
• Lead root cause investigations with Failure Analysis teams.
• Mentor junior engineers and interface with leadership on risk.

Skills

Python

SQL

Data mining

Statistical analysis

Machine learning

Education

Electrical Engineering or related field

Tools

Spotfire

JMP

Job description

Samsung Austin Semiconductor is seeking a Yield Analysis & Improvement Leader to own end-to-end yield performance across wafer sort, parametric, and final test. You will apply data mining, statistics, and machine learning to identify yield limiters, and build automated analytics to correlate defects with inline data.

The role partners with Process Integration, Module, and Design teams to optimize process windows, reduce variability, and drive root cause investigations with FA teams.