Laura Lerner
About Laura Lerner
Laura Lerner is a Core Data Scientist at Optimove in Tel Aviv, Israel, with extensive experience in data management and analytics. She has held various roles in the information technology sector, including positions at Gstat and Data Harbor, and holds a Master's degree in Statistics and an MBA from The Hebrew University of Jerusalem.
Current Role at Optimove
Laura Lerner serves as a Core Data Scientist at Optimove, a position she has held since 2020. In this role, she focuses on leveraging data to drive marketing strategies and enhance customer engagement. Her expertise in data science contributes to the development of predictive models that help businesses understand customer behavior and optimize their marketing efforts.
Previous Experience at Gstat
Laura Lerner worked at Gstat for a total of 11 years, holding multiple roles. She began as a Project Manager from 1997 to 2008, where she managed various projects within the company. Subsequently, she transitioned to the role of Product Manager from 2008 to 2011, overseeing product development and strategy. Additionally, she served as the Head of Statistical Analytics Solutions in R&D from 2008 to 2020, focusing on statistical methodologies and analytics solutions.
Experience at Data Harbor
At Data Harbor, Laura Lerner held the position of Chief Data Scientist from 2011 to 2020. During her tenure, she was responsible for leading data science initiatives and developing analytical frameworks. She also worked in this role in Tel Aviv, Israel, where she contributed to advancing the company's data capabilities and analytics offerings.
Educational Background
Laura Lerner studied at The Hebrew University of Jerusalem, where she earned a Master of Science (MSc) in Statistics and an MBA. Her educational background, spanning from 1984 to 1989, provided her with a solid foundation in statistical analysis and business management, which she applies in her professional roles.
Skills and Expertise
Laura Lerner possesses strong skills in Data Warehousing and Predictive Analytics. Her expertise in Data Warehousing involves the effective storage and management of large volumes of data, facilitating analysis and reporting. In Predictive Analytics, she utilizes statistical techniques to forecast future outcomes based on historical data, enabling informed decision-making in various business contexts.