Saed Qunbar
About Saed Qunbar
Saed Qunbar is a Senior Data Scientist currently working at Workera, where he focuses on innovative upskilling solutions. He has extensive experience in data science and educational research, having held various roles in organizations such as Cognia and the American Board of Pediatrics.
Current Role at Workera
Saed Qunbar serves as a Senior Data Scientist at Workera since 2021. In this role, he focuses on developing innovative upskilling solutions. His work involves leveraging data science techniques to enhance educational experiences and outcomes. He operates remotely from Palo Alto, California.
Previous Experience in Data Science
Before joining Workera, Qunbar worked as a Data Scientist at Cognia from 2019 to 2021. His responsibilities included analyzing data to inform educational strategies. He also held a position as a Visiting Scholar at The American Board of Family Medicine for one month in 2019, contributing to research initiatives in the Lexington, Kentucky area.
Educational Background
Qunbar earned a Doctor of Philosophy (PhD) in Educational Research, Measurement, and Evaluation from the University of North Carolina at Greensboro from 2016 to 2019. He also obtained a Master of Science (MS) in the same field from 2013 to 2015 and a Bachelor of Arts (BA) with Honors in Psychology from 2009 to 2012. He briefly studied at Københavns Universitet - University of Copenhagen in 2011.
Research and Internships
Qunbar has held various research positions, including as a Research and Operational Associate at the American Registry of Radiologic Technologists in 2016. He also served as a Psychometrics Intern at the American Board of Pediatrics from 2016 to 2019. His internship experience includes working as a Research and Measurement Services Intern at Houghton Mifflin Harcourt in 2015 and as a Research and Data Analyst Intern at Credo - Higher Education Consulting from 2014 to 2015.
Contributions to Educational Advancements
In his professional roles, Qunbar has contributed to the development of machine learning and deep learning subsystems aimed at enhancing learning experiences. He has also been involved in designing psychometric subsystems to support educational advancements, reflecting his commitment to improving educational outcomes through data-driven insights.