Jing Yuan L AI
About Jing Yuan L AI
Jing Yuan L is a Data Analyst specializing in Product Quality at ASUS, where he has worked since 2019. He has extensive experience in ETL architecture, data analysis, and machine learning, with a background that includes roles at Yulon Group and Chen Tech Electric.
Work at ASUS
Currently, Jing Yuan L AI serves as a Data Analyst in Product Quality at ASUS, a position held since 2019. In this role, Jing Yuan focuses on enhancing product quality through data analysis and management. The work involves utilizing various data analysis tools and techniques to ensure that products meet quality standards. The experience gained at ASUS contributes to a solid foundation in data-driven decision-making within the technology sector.
Previous Experience at Yulon Group
Before joining ASUS, Jing Yuan worked at Yulon Group as an MIS Engineer, specifically as a Business Intelligence Developer from 2016 to 2018. This role involved developing and implementing business intelligence solutions, which included data management and analysis. The experience at Yulon Group helped in building a strong skill set in data architecture and analytics.
Technical Skills and Specializations
Jing Yuan specializes in building ETL architectures, data cleansing, and preparation using pandas. Proficient in web crawling with Selenium, Jing Yuan also conducts data analysis using scikit-learn and data visualization with Shiny. These technical skills are essential for developing data-driven solutions and improving product quality in the current role.
Education and Expertise
Jing Yuan holds a master's degree in Geotechnical Engineering from National Taiwan University, completed between 2013 and 2015. This educational background provides a strong analytical foundation, which is applicable in data analysis and engineering roles. The expertise gained through academic training complements the practical experience in data analytics.
Projects and Contributions
Jing Yuan has led several significant projects, including the development of a Shiny system as part of a monitoring system project aimed at improving managerial effectiveness. Additionally, Jing Yuan built a recommendation system using a machine learning model (LightGBM) to suggest notebook repair parts based on data from front-end engineers. These projects demonstrate the ability to apply data analytics to real-world challenges.