His research focuses on applying modern ML and data science algorithms to identify unique and interesting structures in data from online and offline environments, to improve consumer experience and business performance. In particular, Shachar’s research seeks to understand and quantify how the vast quantities of data generated through online and offline activities, including posts in online social networks, user-generated content, online search logs and transaction records, can be used to better understand consumption decisions, enhance predictive models aimed at supporting decision-making processes, and optimize business strategies to improve business productivity and efficiency.

His prior research has been published in Journal of Marketing Research, Operations Research, MISQ, Management Science, and Proceedings of ICIS. He received his Ph.D. from Tel Aviv University School of Management and was a Post-Doctoral Associate at MIT Sloan School. He holds B.Sc. and M.Sc. degrees in industrial engineering from Ben-Gurion University.