About me

⚙️ I build AI and ML systems from raw data to production — no handoff required.

That means: data pipeline design, feature engineering, model training and selection, SHAP explainability, MLflow tracking, scoring pipeline deployment, and monitoring. I’ve done this across electric load forecasting (167M row time series), student survival modeling (NLP-enriched, daily batch scoring), genomic survival analysis (~20K features, 1,300 samples), and agentic trading research systems with automated LEAN back testing loops.

The thread across all of it: I treat ML as an engineering discipline. Models ship, pipelines run, results are reproducible, and metrics are tracked.

⚡ Energy & Utilities Domain Expertise:
Anchoring my AI work is decades of experience across every major link in the energy value chain, navigating the strict compliance, safety, and operational mandates of heavily regulated markets. My energy background spans managing complex enterprise systems integration for major utilities like Alliant Energy and Austin Energy (Convergent Group), operating within competitive, newly deregulated transmission and distribution networks (TXU Australia, SP AusNet), and delivering macro energy market analytics (IHS). At the grid edge, I’ve built spatial digital twins and field asset platforms (IQGeo, Field Squared).

Recent technical depth:
🤖 Agentic AI systems — LangChain, tool-calling loops, structured JSON reasoning, multi-step optimization
🗣️ NLP feature engineering — VADER/RoBERTa sentiment, zero-shot distress classification, embedding drift
📈 Time series — LSTM, GRU, RNN, MLP on smart meter data; cyclical feature encoding, seasonal decomposition
🧬 Survival analysis — Cox PH, Gradient Boosting, XGBoost on high-dimensional genomic data
🚀 ML Ops — MLflow, GCP Vertex AI, feature stores, pre-compute pipelines, latency optimization

At Wayfair I built ML platforms at scale — the feature store I launched on Vertex delivered ~$4M in its first year; a 65% inference-latency cut across a 3-model real-time recommendation platform serving 50M+ daily predictions contributed ~$50M in revenue impact.

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