Cuong Thanh-Viet Nguyen

Cuong Thanh-Viet Nguyen has a diverse work experience in the field of data analysis and data science. Cuong started their career as a Data Analyst at Home Credit Vietnam in 2014, where they developed scoring functions and indicators for risk evaluation and created segmentation analysis to control fraud. Cuong then worked as a Data Engineer at Chợ Tốt in 2016, where they monitored and maintained data generating processes and forecasted user behavior. In 2017, they joined Trusting Social as a Data Analyst and Big Data Analyst, where they built and monitored regular reports and conducted ad-hoc analyses using statistical methods. In the same year, they also worked at Gigatum - CLINGME as a Data Scientist, where they built reports, processed crawled data, and built churn prediction models. In 2018, they worked at GiaoHangNhanh as a Data Scientist for a brief period of time before joining INSPECTORIO. At INSPECTORIO, they initially worked as a Research Engineer and later transitioned into the role of Data Scientist. Overall, Cuong Thanh-Viet Nguyen has a strong background in data analysis, data engineering, and data science, with experience in various industries.

Cuong Thanh-Viet Nguyen holds a Master's degree in Mathematics and Computer Science from VNUHCM - University of Science, which they obtained from 2020 to 2022. Prior to that, they earned a Bachelor's degree in Mathematics and Computer Science from the same institution. In addition to their formal education, Cuong has obtained several certifications in various fields of data science and machine learning from Coursera, including Natural Language Processing Specialization in August 2021, Structuring Machine Learning Projects in November 2017, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization in October 2017, Neural Networks and Deep Learning in October 2017, Machine Learning: Classification in November 2016, A Crash Course in Data Science in January 2016, Getting and Cleaning Data in January 2016, R Programming in January 2016, Machine Learning Foundations: A Case Study Approach in November 2015, Machine Learning: Regression in December 2015, Programming for Everybody (Getting Started with Python) in November 2015, Python Data Structures in November 2015, The Data Scientist's Toolbox in January 2016, Customer Analytics in November 2015, Introduction to Big Data in November 2015, and Operations Analytics in November 2015.