Yumei Ye
About Yumei Ye
Yumei Ye is a Data Scientist with a Master's degree in Industrial Engineering from the University of Michigan. Currently employed at Ford Motor Company, Ye has a background in data analysis, statistical visualization, and machine learning, with experience in various roles across multiple organizations.
Work at Ford Motor Company
Yumei Ye has been employed as a Data Scientist at Ford Motor Company since 2020. In this role, she contributes to data analysis and decision-making processes within the organization. Her work involves leveraging her skills in data manipulation and machine learning to support various projects and initiatives in the automotive industry.
Education and Expertise
Yumei Ye earned her Master's degree in Industrial Engineering with a focus on Operations Research from the University of Michigan - Rackham Graduate School, where she studied from 2017 to 2019. She also holds a Bachelor's degree in Aerospace Engineering from Nanjing University of Aeronautics and Astronautics, completed from 2010 to 2014. Her educational background provides her with a strong foundation in data analysis, statistical methods, and engineering principles.
Professional Experience
Prior to her current position at Ford, Yumei Ye worked in various roles that enhanced her data science skills. She served as a Graduate Teaching Assistant at the University of Michigan - Rackham Graduate School, focusing on Big Data Analysis & Visualization. She also held positions as a Quantitative Research Intern at HARMAN International and as a Data Scientist at Owens Corning. Additionally, she worked as a Systems Analyst Engineer for China Southern Airlines and as a System Maintenance Engineer for Shenzhen Airlines.
Technical Skills
Yumei Ye possesses strong technical skills in data analysis and visualization. She is proficient in SQL for data manipulation and extraction from large datasets. Her expertise includes using JMP for statistical analysis and Tableau for creating interactive dashboards. She also has a solid foundation in programming languages such as R and Python, which she employs for machine learning applications, including classification, regression, and clustering tasks.