Dr. Mark De Deuge
About Dr. Mark De Deuge
Dr. Mark De Deuge is the Head of Data Science and Machine Learning at QBE Insurance, where he has worked since 2019. He has extensive experience in data science, having previously served as a Data Scientist at Commonwealth Bank and a Principal Data Scientist at QBE Insurance.
Work at QBE Insurance Group
Dr. Mark De Deuge has been serving as the Head of Data Science & Machine Learning at QBE Insurance since 2019. In this role, he oversees the development and implementation of data-driven strategies to enhance the company's analytics capabilities. Prior to this position, he worked as a Principal Data Scientist at QBE from 2017 to 2019. His responsibilities included building organizational capability and methodologies for data science and analytics, contributing to the company's overall data strategy.
Education and Expertise
Dr. Mark De Deuge holds a Doctor of Philosophy (Ph.D.) in Machine Learning from the University of Sydney, where he studied from 2010 to 2015. His academic focus included Mathematics, Probability Theory, Deep Learning, and Optimization. Earlier, he earned a Bachelor's Degree in Mathematics and Computer Science from the University of Tasmania between 2005 and 2008, followed by a Bachelor of Science (BSc) from the same institution from 2006 to 2009. His educational background provides a strong foundation for his expertise in machine learning and statistical techniques.
Background in Data Science
Before joining QBE Insurance, Dr. Mark De Deuge worked at Commonwealth Bank as a Data Scientist in Research & Development from 2015 to 2017. During his tenure, he established architecture frameworks, standards, policies, and guidelines to enable effective IT governance and solution delivery. He also delivered complex, large-scale projects in robotics and computer vision, showcasing his ability to handle advanced analytics challenges.
Technical Skills and Experience
Dr. Mark De Deuge has extensive experience in the architecture, design, implementation, and operation of advanced analytics platforms. He is proficient in using technologies such as Docker-Swarm and Kubernetes. His deep understanding of machine learning and statistical techniques supports his role in developing innovative data science solutions and methodologies within organizations.