Benjamin Goeing

Benjamin Goeing has a wealth of experience in the technology and consulting industry. Benjamin began their career in 2010 as a Logistics Consulting Intern and Student Employee (part-time) at Deutsche Bahn International. Benjamin then moved on to a Product Management Intern role at Siemens Healthcare in the same year. In 2012, they worked as a Management Consulting Intern at A.T. Kearney, followed by a Management Consulting Intern role at McKinsey & Company in 2013. In 2014, they worked as a Graduate Research Assistant at Stanford University and an iPhone Product Operations Intern at Apple. Benjamin then returned to McKinsey & Company in 2015 as a Senior Consultant, working on autonomous driving, electric vehicle, and connectivity projects. In 2018, they worked as a Graduate Student Researcher (Computer Vision/Deep Learning) and Teaching Assistant at Stanford University, and a Machine Learning/Computer Vision Software Engineer at Meta Co. Benjamin is currently working as a Principal Product Owner, Special Projects Lead, and Engineering Product Manager at Ambient.ai.

Benjamin Goeing has a comprehensive educational background. Benjamin began by completing a Bachelor of Science (B.Sc.) in Computer Science and Management from Technische Universität Berlin in 2009-2013. Benjamin then went on to Stanford University for their Master of Science in Management Science and Engineering from 2013-2015. During this time, they also attended the University of Michigan as an exchange student (non-degree) from 2011-2012. In 2017, they declined an offer of admission to the MBA program at Harvard Business School to pursue further engineering studies at Stanford, where they completed a Master of Science in Electrical Engineering / Computer Science (Focus on Artificial Intelligence and Computer Vision) from 2017-2019. Additionally, they have obtained multiple certifications, including Fundamentals of Digital Image and Video Processing and Mining Massive Datasets from Coursera Course Certificates in 2016 and 2015 respectively, as well as Neural Networks and Deep Learning and Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization from Coursera.