Chandler Qian
About Chandler Qian
Chandler Qian is a Software Engineer currently employed at Thomson Reuters in Toronto, Ontario, Canada. He has a diverse background in machine learning and data science, having previously worked at Scotiabank, Bell, and GEOTAB, and holds multiple degrees in engineering and computer science.
Work at Thomson Reuters
Chandler Qian has been employed at Thomson Reuters as a Software Engineer since 2021. In this role, he applies his expertise in software development and machine learning to contribute to various projects. His work involves utilizing advanced data engineering skills and deploying machine learning models, which aligns with the company's focus on providing data-driven insights and solutions.
Previous Experience at Scotiabank
Prior to his current position, Chandler Qian worked at Scotiabank as the Advanced Analytics Team Lead from 2017 to 2019. During his two years in this role, he was responsible for leading analytics initiatives, leveraging his skills in quantitative methods to drive data-informed decision-making within the organization.
Professional Background at Bell
Chandler Qian served as a Developer in Machine Learning at Bell from 2019 to 2021. His responsibilities included developing machine learning models and algorithms, contributing to the company's technological advancements. This position allowed him to further enhance his skills in programming and machine learning techniques.
Education and Expertise
Chandler Qian holds a Bachelor's degree in Electrical and Mechanical Engineering from Chongqing University. He also earned a Master's degree in Computer Science from Georgia Institute of Technology, completing his studies in 2023. Additionally, he has a Master's degree in Engineering in Electrical and Mechanical Engineering from the University of Ottawa. His educational background provides a strong foundation in both engineering principles and advanced computing.
Technical Skills and Proficiencies
Chandler Qian possesses a diverse skill set in programming languages, including Python, Matlab, C++, and JavaScript. He is proficient in machine learning techniques such as neural networks, CNN, RNN, and LSTM. His expertise extends to deploying machine learning models using cloud technologies like AWS and Sagemaker. He is also experienced in data engineering tools such as Hadoop, Spark, and Hive, which he utilizes for managing large-scale data.