Dvin Galstian
About Dvin Galstian
Dvin Galstian is a Director at jambit, overseeing operations in Yerevan, Armenia, and an Engineering Manager in the Greater Munich Metropolitan Area.
Current Roles at jambit
Dvin Galstian holds dual roles at jambit, serving as the Director in Yerevan, Armenia, and as an Engineering Manager in the Greater Munich Metropolitan Area. In his capacity as Director, he is responsible for overseeing operations at the Yerevan office and is available for questions regarding the office and open positions. As an Engineering Manager, he contributes to the technical leadership and project management of engineering teams in Munich.
Professional Experience in Data Science
Dvin Galstian has extensive experience in data science, having served at jambit in various capacities from 2016 to 2021. Initially joining as a Data Scientist, he was later promoted to Senior Data Scientist. During this period, he led a cross-functional team that developed a machine learning model improving predictive analytics accuracy by 20%. He also organized and led a series of internal workshops on advanced data science techniques.
Previous Positions at Capgemini
Before his tenure at jambit, Dvin Galstian worked at Capgemini. He started as a Software Engineer from 2014 to 2016 and later advanced to Lead Software Engineer, a role he held for eight months. His responsibilities included software development and engineering leadership in the Greater Munich Metropolitan Area.
Educational Background
Dvin Galstian has a diverse educational background, which includes studying Theoretical Physics and Philosophy at Friedrich Schiller University Jena. Additionally, he has completed coursework in Leadership Principles at Harvard Business School, where he earned a certificate. This academic foundation supports his multifaceted role in both technical and leadership capacities.
Research and Presentations
Dvin Galstian has made significant contributions to academia and industry through his research and presentations. Notably, he published a research paper on the application of theoretical physics in data science in a peer-reviewed journal. He also presented at the Munich Data Science Conference on integrating AI into business processes, showcasing his expertise and commitment to advancing the field.