Michael is a casino data scientist and revenue strategist with over 15 years of experience leveraging analytics to drive profit growth, player engagement, and operational efficiency in the gaming industry. His career began in aerospace engineering, but after earning his degree from Cal Poly San Luis Obispo, but after earning his degree from Cal Poly San Luis Obispo, he found himself drawn to the world of gaming and its untapped potential for deeper insights and optimization.

His journey into casino analytics started in 2009 at Pechanga Resort Casino, one of the largest and most successful tribal gaming properties in the U.S. As a financial analyst, he quickly recognized the untapped potential of data science in gaming. By identifying high-value players, optimizing reinvestment strategies, and refining slot floor utilization, Michael played a key role in consistent year-over-year revenue growth of 5-10% during his tenure. His analytical approach to casino marketing—leveraging player behavior, predictive modeling, and machine learning—redefined how casinos identify VIPs and optimize promotions.

Recognizing the limitations of traditional casino analytics, Michael led Pechanga’s initiative to build an internal data science team, hiring and mentoring a team of specialists to push the boundaries of AI and machine learning in gaming. From graph databases for player profiling to AI-driven slot pricing and fraud detection, his work has helped transform casino marketing strategies using predictive insights.

Beyond his professional expertise, Michael is deeply connected to the gaming industry. His wife serves as Treasurer of the Pechanga Band of Indians, and he remains committed to advancing tribal gaming through innovation.

Michael’s strength lies not just in his technical acumen, but in his ability to communicate complex data insights in an engaging, approachable way. His goal is to help casino leaders bridge the gap between raw data and actionable strategy, ensuring the industry stays ahead of evolving player behaviors and market demands.