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 as a Data Scientist at Duke Energy, I moved beyond standard model development to architect the systems that make data science scalable, reliable, and compliant within a highly regulated environment.
My primary objective was to reduce the enterprise "time-to-insight" while enforcing rigorous data governance.
The Feature Store Initiative
The Challenge: Like many large-scale utilities, massive volumes of data—from smart meter readings to grid telemetry and customer usage—were siloed across disparate sources. Data science teams were expending 70–80% of their bandwidth solely on finding, cleaning, and preparing data for each new model.
The Solution: I led the architectural design and implementation of a cloud-native Feature Store utilizing SAS. This system functioned not merely as a database, but as a centralized serving layer, enabling teams to compute a feature (such as "Average Monthly Usage") once and seamlessly reuse it across hundreds of subsequent models.
The Impact:
50% Reduction in Data Prep Time: Drastically accelerated the model development lifecycle and freed up engineering resources.
Single Source of Truth: Established a definition-driven architecture that eliminated logic discrepancies, ensuring all teams calculated metrics like "peak usage" uniformly.
Point-in-Time Correctness: Resolved complex temporal leakage issues inherently present in time-series forecasting.
Governance & Reliability
In the energy industry, a flawed prediction is more than an inconvenience; it can result in regulatory fines or physical grid inefficiencies. I introduced strict software engineering rigor into the data science workflow to mitigate these risks.
Automated Quality Controls: Engineered standards and validation checks to block anomalous or corrupt data before it reached production models.
Drift Monitoring: Implemented comprehensive MLOps protocols to monitor statistical shifts in grid data, replacing silent pipeline failures with proactive, automated alerts.
Experimental Design: Elevated statistical rigor by deploying control group methodologies, ensuring mathematical proof of value for every deployed model.
Driving Business Value at Scale
Technical architecture must ultimately serve business objectives. Beyond building the underlying infrastructure, I deployed predictive models that directly optimized operations and drove revenue.
Marketing Optimization: Delivered predictive scoring mechanisms to optimize over 200,000 direct mail touchpoints, ensuring marketing budgets were efficiently allocated strictly to high-propensity customers.
Forecasting Accuracy: Enhanced the reliability and accuracy of key enterprise forecasting solutions through the implementation of rigorous back-testing and validation frameworks.