Executive Bio
I lead applied AI and data science programs by connecting strategy, experimentation, and production delivery. The focus is always the same: make the work trusted enough to change decisions at executive level.
My work sits at the intersection of AI strategy, operational rigor, and commercial accountability. Over the last 12+ years, I have built systems that help large organizations move from analytical possibility to repeatable business action.
I have led data science across forecasting, personalization, experimentation, and ML-enabled decisioning in retail and fintech settings. The through-line is consistent: models only matter when the surrounding organization can trust, operationalize, and measure them.
That is why I focus as much on decision layers, operating cadence, and cross-functional alignment as I do on the technical stack. Durable value comes from the system, not from isolated technical wins.
The job is not to produce clever model outputs. It is to make those outputs usable, trusted, and operational inside real teams.
Mature machine learning programs win when delivery becomes dependable enough for the business to plan around it.
I build teams that can prove impact with experiments, operating metrics, and clear investment logic.
Most organizations invest heavily in model development, then underinvest in the layer that translates scores into action. My framework for closing that gap has three parts.
Produce trustworthy model outputs with clear business intent, not generic predictions disconnected from use cases.
Define the rules, constraints, and prioritization logic that convert signal into a business action teams can actually execute.
Measure outcomes, inspect reliability, and feed what is learned back into roadmap and organizational decisions.
Amir began his career in Machine Learning after earning a Master's in Computer Science from McMaster University. His thesis focused on developing a Natural Language Processing engine to triage messages between patients and physicians, and his research was presented at conferences including AMCIS and IBM CASCON.
He then spent seven years at Canadian Tire as a Lead Data Scientist, applying Machine Learning to improve retail performance across multiple domains. He later joined The Home Depot's eCommerce team, where he worked on search optimization and recommender systems. Amir subsequently served as Machine Learning Lead at Paytm, contributing to large-scale personalization initiatives across the fintech platform.
Currently, Amir is Director of Data Science at Sobeys, one of Canada's largest food retailers, where he leads data science initiatives focused on advancing marketing strategy and driving measurable business impact.