Anmol Garg
About Anmol Garg
Anmol Garg is a Data Scientist at SOTI in Mississauga, Ontario, Canada, with a background in machine learning and engineering. He has previously held positions at the University of Toronto and Larsen & Toubro, and he holds a Master of Engineering from the University of Toronto.
Work at SOTI
Anmol Garg has been employed as a Data Scientist at SOTI since 2021. In this role, he focuses on developing deep machine learning models for deployment on Samsung devices. His work utilizes Samsung's Knox framework, emphasizing encrypted version management with run, update, and delete APIs. His contributions to the company are centered around enhancing the functionality and security of mobile applications.
Education and Expertise
Anmol Garg holds a Master of Engineering (MEng) in Industrial Engineering from the University of Toronto, where he studied from 2019 to 2021. He also earned a Bachelor's in Mechanical Engineering from Vellore Institute of Technology, completing his degree from 2012 to 2016. His educational background equips him with a strong foundation in engineering principles and data science methodologies.
Background
Anmol Garg's professional journey includes various roles in academia and industry. Before joining SOTI, he worked as a Graduate Research Assistant at the University of Toronto's Rotman School of Management for five months in 2020. He also served as a Teaching Assistant at Vellore Institute of Technology in 2013 and held multiple positions at Larsen & Toubro, including Scheduling Engineer and Project Planning Analyst between 2016 and 2019.
Achievements
During his career, Anmol Garg has successfully implemented machine learning projects that automate monitoring and rectification of cloud server issues across AWS, Azure, and GCP. He has utilized Power BI to create reports that track the accuracy of machine learning models, achieving approximately 90% accuracy in predictions. His specialization in customer churn prediction employs advanced modeling techniques, resulting in a retention prediction probability of around 90%.