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MTB PWF MRDA APPLICATION ENGINEER LEAD at Micron Technology
Taichung, , Taiwan -
Full Time

Start Date

Immediate

Expiry Date

12 Jan, 26

Salary

0.0

Posted On

14 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Data Analysis, Filtering Strategies, Execution Efficiency, Performance Metrics, Programming Languages, Data Cleaning, Feature Engineering, Model Evaluation, Performance Tracking, Software Development Practices, Code Review Processes, Communication Skills, Cross-Functional Collaboration, Front-End Development, Back-End Development, Semiconductor Process Data

Industry

Semiconductor Manufacturing

Description
Oversee and manage team progress across multiple projects. Mentor engineers on data analysis techniques, filtering strategies, execution efficiency, and performance metrics. Write and review code to ensure quality and maintainability. Possess a working knowledge of both front-end and back-end system architecture. Collaborate effectively with other domain experts to drive cross-functional solutions. Support the development and refinement of data pipelines and analytical models to improve operational outcomes. Master's degree or higher in Data Science, Electrical Engineering, Computer Science, or related fields. Minimum 5 years of experience in semiconductor or manufacturing-related data analytics. Proficient in programming languages such as Python, R, or equivalent. Strong skills in data cleaning, feature engineering, model evaluation, and performance tracking. Solid understanding of software development practices and code review processes. Excellent communication and cross-functional collaboration skills. Experience with both front-end and back-end development. Familiarity with semiconductor process data, SPC/OOC analysis, and yield prediction applications.
Responsibilities
Oversee and manage team progress across multiple projects while mentoring engineers on data analysis techniques. Collaborate with domain experts to drive cross-functional solutions and support the development of data pipelines and analytical models.