Minnie Nguyen
About Minnie Nguyen
Minnie Nguyen is a Data Scientist based in Montreal, Quebec, Canada, currently employed at Intact since 2020. She has a background in Mathematics and Computer Science from McGill University and has held various roles in data science and software development.
Current Role at Intact Financial Corporation
Minnie Nguyen currently serves as a Data Scientist at Intact Financial Corporation, a position she has held since 2020. In this role, she focuses on research and experimentation within the machine learning lifecycle, with a particular emphasis on natural language processing (NLP). Her work involves developing and implementing data-driven solutions that enhance the company's analytical capabilities.
Previous Experience at Dynamicly
Minnie Nguyen began her career at Dynamicly, where she worked as a Data Scientist Intern for three months in 2019. Following this role, she was promoted to Junior Data Scientist, where she contributed for seven months until 2020. During her time at Dynamicly, she gained practical experience in data analysis and model development.
Educational Background at McGill University
Minnie Nguyen earned her Bachelor of Science degree in Mathematics and Computer Science from McGill University. She studied from 2015 to 2019, acquiring a solid foundation in mathematical principles and computational techniques that support her work in data science.
Internship Experience in Data Science and Software Development
In addition to her roles at Dynamicly, Minnie Nguyen completed a Data Science Internship at Intact in 2020 for three months. Prior to that, she gained experience as an Android Developer Intern at VietIS Corporation in 2017 and as a Software Developer at the Schulich School of Music of McGill University in 2018. These internships provided her with diverse skills in software development and data analysis.
Projects and Technical Skills
Minnie Nguyen has developed a personal website that showcases her projects related to data science and natural language processing. One notable project includes the development of a stacking ensemble pipeline designed to combine tree-based models for predicting loss amounts. This project highlights her technical skills and her ability to apply machine learning techniques to real-world problems.