Meg Zhou

Meg Zhou is an accomplished data scientist with extensive experience in predictive modeling and data analysis across various industries. Currently serving as a Data Scientist Fellow at Techlent since August 2022, Meg Zhou has led the development of a detection engine for predicting loan default rates, utilizing advanced classification models such as Logistic Regression, Random Forest, GBT, and XGBoost. Prior roles include Senior Data Scientist/Analyst at Axens, where Meg Zhou built a forecasting engine for catalyst storage stability, and Senior Data Analyst/Scientist at Heraeus, focusing on consumption prediction models. Earlier career highlights include consulting for Johnson Matthey and data analysis at W.R. Grace and SINOPEC Research Institute, with significant achievements in enhancing production processes and product profitability. Meg Zhou holds a Master of Engineering in Chemical Engineering from the University of Waterloo and a Bachelor's Degree in Polymer Science from Beijing University of Chemical Technology.