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Most organizations struggle with process improvement. They either move too slowly, optimize the wrong things, or fail to create sustainable change. After years of leading engineering and data science teams, including most recently as VP of Engineering & Data Science at HealthRhythms, I’ve discovered that mastering feedback loops is the hidde...

In the early stages of building AI products, uncertainty reigns supreme. Leaders, managers, and individual contributors often grapple with defining their path forward. This uncertainty isn’t a flaw—it’s a feature of early-stage AI development. The key to success lies not in having all the answers, but in building an effective experimentation min...

Over my career leading engineering and data science teams — from regulated industries to early-stage startups to large enterprises — I’ve consistently seen one pattern: Teams can move incredibly fast when they understand how their work drives business value, and painfully slow when they don’t. Most recently, as VP of Engineering and Data Scienc...

The Power of Semantically-Meaningful and Human-Readable Representations As AI and data science leaders, we often focus on sophisticated algorithms and cutting-edge technologies. However, one fundamental yet frequently overlooked aspect can make or break your AI initiatives: how your data is represented. Let me share a practical guide that will ...