Building production AI/ML, forecasting, and decision systems that drive measurable business impact.
Principal Data Scientist with 10+ years designing, building, and deploying production-grade machine learning, optimization, forecasting, and AI solutions across retail, healthcare, and public-sector environments.
What I Do
Core capability areas across applied AI/ML and decision science.
Applied AI & LLM Solutions
Agentic RAG systems, LLM fine-tuning (LoRA/QLoRA), prompt orchestration, evaluation workflows, and production-oriented AI architectures.
Machine Learning & Predictive Analytics
Production ML pipelines for recommender systems, false in-stock detection, classification, and predictive analytics using XGBoost, LightGBM, and CatBoost.
Forecasting & Time-Series
Time-series forecasting for capacity planning and demand prediction using STL, TBATS, dynamic harmonic regression, LSTM, and ensemble methods.
Optimization & Decision Systems
Staff scheduling optimization (Gurobi), discrete-event simulation, dynamic programming, and decision-support systems delivering measurable cost savings.
About
I'm a Principal Data Scientist with a PhD in Operations Research and 10+ years of experience designing and deploying production-grade AI/ML solutions. Currently at Canadian Tire Corporation, I build recommender systems, production ML models, optimization engines, and agentic AI systems across retail operations.
My work spans the full analytical lifecycle — from data ingestion and feature engineering to model development, validation, deployment, and stakeholder communication. I bring deep expertise in Python, SQL, Spark, Databricks, and MLflow, with a strong track record of translating ambiguous business problems into scalable solutions and measurable outcomes.