Zihao Wang
About Zihao Wang
Zihao Wang is a Data Analyst specializing in Financial Planning and Analysis (FP&A) with a background in business analytics and data modeling. He has experience working with various organizations, including the University of California, Irvine, and Experian, where he developed predictive models for consumer bankruptcy likelihood.
Work at Insight Global
Zihao Wang has been employed at Insight Global as a Data Analyst in Financial Planning and Analysis (FP&A) since 2021. His role involves analyzing financial data to support decision-making processes within the organization. He has contributed to various projects that enhance the financial performance and strategic planning of the company. His experience in data analysis is leveraged to provide insights that drive business outcomes.
Education and Expertise
Zihao Wang earned a Master of Science in Business Analytics from the University of California, Irvine - The Paul Merage School of Business, achieving a GPA of 3.95/4.0. His studies focused on data analytics and its application in business contexts. Prior to this, he obtained a Bachelor's degree in Business Administration with a concentration in Information Systems and Decision Science from California State University-Fullerton, where he developed foundational knowledge in data management and analysis.
Background
Zihao Wang has a diverse background in data analysis and business analytics. He began his career as a Business Analyst Intern at Houghton Street Consulting Limited in Shanghai, China, in 2019. He later served as a Research Assistant at Cal State Fullerton's College of Business and Economics from 2019 to 2020. In 2021, he worked as a Graduate Research Assistant at the University of California, Irvine, and as a Data Analyst Practicum at Experian, where he developed predictive models for consumer bankruptcy.
Achievements in Data Analysis
During his tenure at Experian, Zihao Wang developed ensembled models using LightGBM and Catboost to predict consumer bankruptcy likelihood. His models outperformed the benchmark logistic regression model by 3% in accuracy. He also utilized feature selection methods to identify the top 10 valuable features from over 700, which significantly enhanced model performance. His ability to present complex data insights was demonstrated when he delivered an executive summary to company stakeholders, outlining the value proposition and model deployment.