Sébastien Ouellet

Machine Learning Developer @ Kinaxis

About Sébastien Ouellet

Sébastien Ouellet is a Machine Learning Developer with a background in Cognitive Science and Computer Science. He has experience in building data infrastructures and applying machine learning to meteorological and geospatial data problems.

Work at Kinaxis

Sébastien Ouellet has been employed at Kinaxis as a Machine Learning Developer since 2018. In this role, he focuses on the application of machine learning techniques to enhance operational efficiencies and decision-making processes within the organization. His work contributes to the development of advanced analytics solutions that support supply chain management.

Education and Expertise

Sébastien Ouellet holds a Bachelor's degree in Cognitive Science from Carleton University, where he studied from 2010 to 2013. He furthered his education at Université de Montréal, earning a Master's degree in Computer Science from 2013 to 2015. His academic background is complemented by various coursework in Physics from the University of Ottawa, completed between 2007 and 2010. He possesses expertise in machine learning applications, particularly in meteorological and geospatial data.

Background

Sébastien Ouellet began his professional journey as a Student Researcher at Carleton University in 2011, where he worked for seven months. He later served as a Graduate student in Computer Science at Université de Montréal from 2013 to 2015. Additionally, he gained experience as a Summer student on the Web team at Canada Council for the Arts in 2013. Prior to his current role, he worked as an AI Specialist at AZUR Group inc. from 2017 to 2018.

Professional Experience

Throughout his career, Sébastien Ouellet has developed a strong foundation in building data infrastructures, particularly within cognitive and physiological domains. His experience spans various roles, including his time at AZUR Group inc. as an AI Specialist, where he focused on artificial intelligence applications. His diverse background equips him with a unique perspective on integrating machine learning into practical solutions.

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