Mohamed Azar El Mourabit

Mohamed Azar El Mourabit is a Senior Data Scientist with experience in cybersecurity and fintech risk. At Glovo, Mohamed focuses on cash risk modeling, driving unpaid from -0.8% GMV to 0. At WatchGuard Technologies, Mohamed used unsupervised machine learning to detect suspicious behavior in enterprise networks, reducing incident response costs by 700k€ per year. While at Panda Security, Mohamed developed a malware classification model with 99.86% precision through deep learning, automating the review of 5 million files annually. Mohamed holds a Master of Science in Data Science from Universitat Oberta de Catalunya, a Bachelor of Applied Science in Telecommunication from the University of Valencia, and completed a Machine Learning program with Andrew Ng at Stanford University.