Nirmal Roy
About Nirmal Roy
Nirmal Roy is a Data Scientist at TELUS and an Instructor & Course Developer at the University of Toronto School of Continuing Studies, specializing in feature engineering in machine learning and artificial intelligence.
Company
Nirmal Roy works as a Data Scientist at TELUS in the Toronto, Canada Area. He is also employed at the University of Toronto School of Continuing Studies, serving as an Instructor and Course Developer in Feature Engineering in Machine Learning & Artificial Intelligence.
Title
Nirmal Roy holds the title of Data Scientist. He actively contributes to the academic and professional community through his dual roles at TELUS and the University of Toronto School of Continuing Studies.
Education and Expertise
Nirmal Roy has a robust educational background. He studied Big Data Analytics at the Indian Institute of Management Bangalore and completed an Executive Program in Business Analytics at the Indian Institute of Management, Calcutta. He holds a BSc(H) in Statistics from Delhi University. His technical skills include proficiency in SAS, R, Python, and Pyspark, alongside hands-on expertise in statistical techniques such as logistic regression, decision trees, random forests, gradient boosting, SVMs, neural networks, KNN, ensembling, and clustering.
Background
Nirmal Roy has accumulated over a decade of experience in applying data science to solve complex business problems across various industries. His work history includes positions at Max Life Insurance Company Limited, where he served in roles ranging from Deputy Manager to Senior Manager, and at Wipro BPO as a Senior Business Analyst. He has also held various analytical positions at WNS Global Services and began his career as a Quality Analyst at Infosoft Technologies.
Developed and Taught Course on Feature Engineering
Nirmal Roy developed and teaches a course on Feature Engineering in Machine Learning & Artificial Intelligence at the University of Toronto School of Continuing Studies. His role involves creating course content, instructing students, and imparting practical knowledge, effectively bridging the gap between theoretical and practical applications in the field of machine learning.