I solve complex AI and data science problems by finding patterns others miss across industries.
Specializing in AI strategy, machine learning implementation, and GenAI solutions that transform business outcomes through cross-industry pattern recognition.
AI Strategy & Data Science Expertise
In machine learning and AI strategy, there’s never just one solution to a problem. Some approaches are simply more elegant than others.
This drives how I approach complex data science challenges. After 14 years implementing AI solutions across automotive, retail, insurance, FMCG, and telecommunications, I’ve learned that breakthrough ML insights often come when you apply data science methodologies from completely different industries.
Real-World AI Implementation
When I was leading an AI strategy project for automotive inventory optimization, traditional machine learning approaches weren’t delivering results. The data patterns were complex, requiring advanced analytics and GenAI techniques. Then I applied healthcare data science insights to the automotive challenge.
The AI solution became clear. We eliminated 45,000 excess units through end-to-end ML implementation. Not because I knew more about automotive than the experts, but because I applied cross-industry AI strategy thinking.
What I Do
I work with Fortune 500 companies implementing comprehensive AI strategy and data science solutions. My expertise spans machine learning architecture, GenAI implementation, predictive analytics, and end-to-end AI transformation.
Currently serving as Acting CTO across multiple accounts, developing AI strategies that deliver measurable business impact. I also mentor 25+ AI and data science professionals in advanced machine learning techniques and strategic AI thinking.
My background combines mechanical engineering, applied mathematics, and 14+ years of hands-on AI/ML implementation. The analytical frameworks that work for complex data science problems often apply beautifully to AI strategy across different business domains.
AI & Machine Learning Philosophy
Pattern recognition across domains. Root cause analysis through data science. Elegant AI solutions over brute force machine learning approaches.
When you’ve solved similar ML challenges in healthcare, the path through retail AI becomes visible. When you understand the mathematics of one optimization problem, you start seeing the same data science structures everywhere.
It’s not about being the smartest AI expert in the room. It’s about being the data scientist who’s seen this pattern before, just wearing different industry clothes.
Want to discuss your AI strategy or data science challenges?