Powering the Grid with Intelligent Data Architectures

The Intersection of Utilities & MLOps

In the energy sector, data isn't just an asset—it is the digital infrastructure that supports the physical grid. During my tenure at Duke Energy, I moved beyond simple model building to architecting the systems that make data science scalable, reliable, and compliant in a highly regulated environment.

My focus wasn't just on predicting outcomes; it was on reducing the "time-to-insight" for the entire enterprise.

The Challenge: Like many large-scale utilities, we had massive amounts of data (smart meter readings, grid telemetry, customer usage) siloed across disparate sources. Data Scientists were spending 70-80% of their time just finding and cleaning data for every single new model.

The Solution: I led the architectural design and implementation of a cloud-native Feature Store on AWS. This wasn't just a database; it was a centralized serving layer that allowed teams to compute a feature (like "Average Monthly Usage") once and reuse it across hundreds of models.

The Impact:

• 50% Reduction in Data Prep Time: Drastically accelerated the model development lifecycle.

• Single Source of Truth: Eliminated logic discrepancies where different teams calculated "peak usage" differently.

• Point-in-Time Correctness: Solved c

• mplex temporal leakage issues inherent in time-series forecasting.

Governance & Reliability

In energy, a bad prediction isn't just an inconvenience. It can lead to regulatory fines or grid inefficiencies. I introduce software engineering rigor to the data science workflow.

• Automated Quality Controls: Developed standards to block bad data before it ever hit a model.

• Drift Monitoring: Implemented MLOps protocols to detect when statistical properties of the grid data shifted, triggering automated alerts rather than silent failures.

• Experimental Design: Improved statistical rigor by implementing control group methodologies, ensuring that when we claimed a model added value, we could prove it mathematically.

Architecture

Technical architecture must serve business goals. Beyond infrastructure, I deployed predictive models that directly optimized operations and revenue.

• Optimization at Scale: Delivered predictive scores to optimize 200,000+ direct mail touches, ensuring marketing budget was spent only on customers likely to engage.

• Forecasting Accuracy: Enhanced the reliability of key forecasting solutions through rigorous back-testing and validation frameworks.