Romario Vaz
About Romario Vaz
Romario Vaz is a Data Scientist based in Hong Kong SAR, currently employed at Intact since 2020, where he focuses on data-driven solutions for commercial lines underwriting. He has a background in computer science from The Chinese University of Hong Kong and the University of Waterloo, and has experience in machine learning and deep learning NLP models.
Work at Intact Financial Corporation
Romario Vaz has been employed as a Data Scientist at Intact Financial Corporation since 2020. In this role, he focuses on developing data-driven solutions aimed at enhancing commercial lines underwriting. His work involves collaboration with actuaries and software engineers to create machine learning solutions that address specific business needs.
Education and Expertise
Romario Vaz holds a Bachelor of Science degree in Computer Science from The Chinese University of Hong Kong, where he studied from 2016 to 2020. He also participated in an exchange semester at the University of Waterloo in 2019, further expanding his academic experience. His educational background provides a solid foundation for his expertise in data science and machine learning.
Background in Research and Development
In 2019, Romario Vaz worked as a Summer Research Intern at The Chinese University of Hong Kong for two months. During this internship, he gained practical experience in research methodologies. Additionally, he participated in the HKSTP InnoAcademy - Technology Leaders of Tomorrow (TLT) Programme in 2020, where he engaged in technology-focused initiatives for two months.
Startup Experience at European Innovation Academy
In 2018, Romario Vaz co-founded a startup while participating in the European Innovation Academy in Turin, Piedmont, Italy. This experience allowed him to explore entrepreneurial ventures and develop skills in startup development within a collaborative environment.
Machine Learning and NLP Expertise
Romario Vaz has developed machine learning solutions that incorporate deep learning natural language processing (NLP) models, including BERT. His work in this area aims to enhance statistical models and improve data analysis capabilities, showcasing his technical proficiency in advanced data science techniques.