Rhoda Pang
About Rhoda Pang
Rhoda Pang is a Business Intelligence Analyst based in Houston, Texas, currently working at CenterPoint Energy and Selleck Chemicals LLC. She has a background in data science and human resources, with experience in machine learning and high-tech recruiting.
Work at CenterPoint Energy
Rhoda Pang has been employed as a Business Intelligence Analyst at CenterPoint Energy since 2022. In this role, she applies her analytical skills to support data-driven decision-making processes within the organization. Her responsibilities include analyzing data trends and providing insights that contribute to the company's operational efficiency.
Current Role at Selleck Chemicals LLC
In addition to her position at CenterPoint Energy, Rhoda Pang works as a Business Analyst and Customer Support representative at Selleck Chemicals LLC. She has held this role since 2021, where she focuses on understanding customer needs and providing data analysis to support business operations.
Education and Expertise
Rhoda Pang has a strong educational background in data science and business administration. She studied at Springboard, completing the Data Science Career Track from 2020 to 2021. She also earned a Master's degree in Open Digital Innovation from Purdue University in 2020. Additionally, she holds a Bachelor of Business Administration in Human Resources Management from Shenzhen University, obtained in 2011.
Professional Experience in Recruitment
Rhoda Pang has experience in recruitment, having worked as a Recruiter at Hui Associates Limited from 2013 to 2016 and at Hyper Growth Management Consulting (Beijing) Ltd. for 9 months in 2016. During her time in these roles, she focused on high-tech recruiting for major firms such as Alibaba, Tencent, and Huawei, which enhanced her communication and interpersonal skills.
Data Analysis and Machine Learning Projects
Rhoda Pang has engaged in various data analysis projects, notably analyzing the Lending Club dataset to predict loan default rates using machine learning techniques. She developed models such as logistic regression, SVM, random forest, and gradient boosting to predict loan delinquency status over 90 days. Furthermore, she utilized natural language processing (NLP) to analyze text datasets for customer interest classification.