Experience in AI & Machine Learning
I’ve built AI-driven products that are not just innovative—but enterprise-ready. My deep background in data governance and experience transforming the most innovative companies in the world gives me a unique edge in the AI space. I understand creating scalable governance frameworks and I bring proven skills to transform an organization.
At Google and GE, I built enterprise data programs from the ground up—improving data quality, engineering new workflows, and architecting governance frameworks that were meant to scale. These weren’t side projects—they were the backbone of transformation initiatives that unlocked machine learning insights, accelerated ERP adoption, and enabled future enterprise level programs.
Now, I bring that lens to Generative AI: combining technical depth with user-centric design to build tools that automate decisions, elevate productivity, and integrate seamlessly with enterprise ecosystems. From building AI tools to predict cost to move large machine to creating data programs at scale, I’ve consistently delivered solutions that balance innovation with real-world impact.
Solving Problems at Scale
Predictive Modeling to Accelerate NPI
I led the development of a machine learning–powered "Should Cost" tool to support GE's NPI (New Product Introduction) teams. By integrating product definition data from PLM systems and applying regression-based models, we were able to predict component pricing based on material, geometry, manufacturing process, and other attributes. This gave sourcing and engineering teams a data-backed baseline for vendor negotiations—improving cost visibility, speeding up procurement, and reducing overpayment risk early in the product lifecycle.
*Pending Launch
Smart Optimization of Equipment Rentals
To reduce GE’s operational cost of large-scale industrial services projects, I partnered with the Machine Learning team to build an ML model that optimized the logistics of high-cost equipment rentals, such as cranes. Rather than renting equipment per project, the algorithm dynamically analyzed rental costs versus relocation costs, factoring in location, scheduling overlap, potential shipping lanes - including transport complexity. The model provided actionable insights that enabled executives in the Services to make informed decisions based on real data—leading to significant savings and smarter resource planning.
Meeting Customers Where They Are
While at Apple, to improve data accessibility and adoption of a newly implemented business glossary and reference data tool, we built a Slack-integrated chatbot that allows employees to query data definitions directly within their daily workflows. The chatbot serves as a user-friendly interface to the enterprise data governance platform, giving teams real-time answers to “What does this field mean?” and “Where does this data come from?”—improving data literacy, consistency, and decision-making across the organization. *