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John Mendez

Global Practice Head at Visual Collaboration Technologies

John Mendez has over 12 years of professional experience in the IT industry. John began their career in 2007 as a Software Engineer for IBS Software Services. During their two-year tenure, they were promoted to Executive - Global Marketing (Europe & MEA). In 2009, they joined Cochin University of Science and Technology as a student, where they were a core committee member for Talentime 2010, a marketing and design team lead, and won prizes in marketing games and a rock band competition. In 2011, they joined Unisys as Technology Business Development - Stealth Security & ClearPath Forward, Account Management - ClearPath Servers, and Business Excellence. In 2014, they joined InfoVision Inc. as Global Practice Head, Director - Technology Solutions, Senior Manager - Technology Solutions, and Marketing & Business Development Manager (Technology Solutions). In 2019, they joined Visual Collaboration Technologies, Inc. as Global Practice Head.

John Mendez has a diverse educational background. John completed a B-Tech in Engineering from Mahatma Gandhi University from 2003-2007. John then earned a Master of Business Administration (MBA) in Marketing and Operations from Cochin University of Science and Technology from 2009-2011. Most recently, they attended MIT Sloan School of Management in 2011-2012 to study Business Strategy. Additionally, they have obtained numerous certifications from LinkedIn, including Agile Marketing Foundations, Agile Software Development: Scrum for Developers, Artificial Intelligence Foundations: Machine Learning, Creating Your IT Strategy, Inside Sales, Machine Learning & AI: Advanced Decision Trees, Strategic Thinking, Transitioning from Data Warehousing to Big Data, Disrupting Yourself, Artificial Intelligence Foundations: Thinking Machines, Cloud Architecture: Core Concepts, Digital Transformation, Machine Learning & AI Foundations: Recommendations, Microservices and Azure Service Fabric Basics for Developers, and The Essential Elements of Predictive Analytics and Data Mining.

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