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Samuel Mueller is the co-founder and CEO of Scandit, a global leader in smart data capture technology. With a deep background in computer science and technology, holding a PhD from ETH Zurich and having served as a visiting scientist at MIT, Samuel has been a driving force in mobile computer vision. He leads Scandit's mission to empower enterprises by blending the physical and digital worlds, transforming everyday smartphones into powerful barcode scanning and data capture devices. Under his leadership, Scandit has grown into a tech 'unicorn,' providing innovative solutions for major companies across the retail, transportation, logistics, and healthcare sectors worldwide.
Samuel Mueller's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Co-founded Scandit in 2009 and, as CEO, has scaled the company into a global leader in smart data capture. Under his leadership, Scandit achieved a valuation of over $1 billion in its Series D funding round in 2022, attaining 'unicorn' status.
Led the development and commercialization of Scandit's core technology, which transforms standard smartphones into enterprise-grade barcode scanners and data capture tools, democratizing this capability for businesses of all sizes.
Successfully secured multiple significant funding rounds, including a $150 million Series D round led by Warburg Pincus, demonstrating strong investor confidence in his vision and Scandit's market leadership.
Holds a PhD from the prestigious ETH Zurich and was a visiting scientist at MIT, where he contributed to research in computer vision and ubiquitous computing, laying the academic groundwork for Scandit's technological innovations.
ETH Zürich - Year 2005
University of Zurich - Year 2000
Lund University - Year 2003
University of Zurich - Year 2000
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Scandit is a technology platform specializing in smart data capture, empowering businesses to transform everyday devices like smartphones and drones into enterprise-grade barcode scanners and data capture tools. Their solutions utilize computer vision, augmented reality (AR), and machine learning to rapidly and accurately scan barcodes, text (OCR), and objects. This technology is widely used by end-users in retail for tasks like mobile point-of-sale and inventory management, in transport and logistics for proof of delivery and asset tracking, and in healthcare for patient identification and medication administration, ultimately streamlining workflows and enhancing operational efficiency.
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