Michael Minar
Palo Alto, California, United States
Summary
👤
SeniorMichael Minar is a Palo Alto-based data science leader focused on building data products that transform brick-and-mortar retail. He combines a Stanford Applied Physics PhD with deep expertise in quantitative modeling and applied machine learning on large data sets to deliver actionable insights for marketing, merchandising, and operations. Currently a principal on Apple Pay's data science team, he has previously led data science and ML at Trifacta and directed analytics at Euclid, helping retailers quantify offline impact and optimize store performance. His work spans recommender systems, signal processing, distributed sensor networks, and event detection, with hands-on fluency in Python, SQL, Redshift, Scala, Spark, and data visualization. He excels at turning data into products and compelling stories, translating complex requirements into practical, auditable solutions.
11 years of coding experience
Github Skills (8)
masters10
data-source9
science8
python7
pandas7
machine-learning7
data-analysis6
data-science6
Github contributions (1)
datasciencemasters/go
May 2014 - May 2014
Contributions:3 commits in 12 days
data-sourcedata-analysispythonmastersscience
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