Scott Hind
About Scott Hind
Scott Hind is a Machine Learning Engineer at Unbounce in Vancouver, British Columbia, with extensive experience in data analysis, geomatics, and geographic information systems. He holds a Master of Science in Computer Science from Simon Fraser University and has presented research at the Canadian Conference on Artificial Intelligence.
Company
Scott Hind is currently working at Unbounce, a prominent company based in Vancouver, British Columbia, Canada. Unbounce specializes in creating tools for building and optimizing landing pages. Scott has been with Unbounce since 2020, focusing on machine learning engineering.
Title
Scott Hind holds the title of Machine Learning Engineer. In this role, he is responsible for developing advanced machine learning models that improve the accuracy of predictive analytics for the company. He has also served as a Data Engineer (Co-op) and Data Analyst (Co-op) at Unbounce in 2019.
Education and Expertise
Scott Hind has an extensive educational background from Simon Fraser University. He earned a Master of Science (MS) in Computer Science from 2018 to 2020, a Bachelor of Science (BS) in Computer Science from 2017 to 2018, and a Certificate in Spatial Information Systems from 2014 to 2017. Additionally, he holds a Bachelor of Arts (BA) in Geography with a Co-operative Education component, completed from 2011 to 2017.
Previous Work Experience
Before his current role at Unbounce, Scott Hind gained diverse experience in the tech and GIS fields. He worked as a Geomatics Technologist (Contract) and (Co-op) at the Royal Canadian Mounted Police from 2016 to 2018. He also served as a Geographic Information Systems Coordinator (Co-op) at Fisheries and Oceans Canada in 2015, and as a GIS Technician (Co-op) at BC Agricultural Land Commission in 2014. Additionally, Scott was a Developer & Research Assistant at Simon Fraser University for five years from 2014 to 2019.
Achievements
Scott Hind has notable achievements in his professional and academic careers. At Unbounce, he developed machine learning models that significantly improved the accuracy of predictive analytics. He presented a paper on spatial data analysis at the Canadian Conference on Artificial Intelligence in 2019. Additionally, he contributed to an open-source project focused on enhancing geographic information systems (GIS) capabilities. In 2016, he received the 'Outstanding Co-op Student' award from Simon Fraser University. Furthermore, Scott actively participates as a mentor for aspiring data scientists through a local tech community in Vancouver.