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    Michael Minar

    Apple Pay at Apple

    Michael Minar is a Principal with extensive experience in building data products that are transforming brick and mortar retail. His primary expertise lies in quantitative modeling and applied machine learning on large datasets, focusing on enhancing customer service for store owners/operators. With a background in experimental science and mathematics/statistics, including a Stanford Applied Physics PhD, Michael excels in data analysis, machine learning, feature extraction, and rapid prototyping. He specializes in applied machine learning, recommender systems, analytics, data science, and data-driven products, with skills in Python, SQL, Redshift, Scala, Apache Spark, and data visualization. Michael has a strong track record of turning data into actionable insights and products.

    Michael Minar's educational background includes a Doctor of Philosophy (PhD) in Applied Physics from Stanford University and a Bachelor of Science (B.S.) in Engineering Physics from Case Western Reserve University. His professional experience spans roles at renowned companies such as Apple, Trifacta (as Head of Data Science and Machine Learning, Principal Engineer), Euclid (as Director of Analytics, Analytics Scientist), where he has honed his expertise in signal processing, distributed sensor networks, algorithms, event detection, and tracking. Michael is known for his contributions to developing algorithms that power marketing, merchandising, and operational solutions in retail. His work involves creating products and insights to optimize store operations and customer experiences.

    Michael Minar
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    Location

    Stanford, California, United States