You will help build systems that: estimate causal response + quantify uncertainty → choose actions → generate helpful information → observe outcomes → update policies → evaluate challengers → deploy within guardrails Can we estimate the value of a challenger policy before fully deploying it? How do we optimize economic outcomes while respecting inventory, margin, vendor, customer, and operational constraints? policy learning and constrained optimization; counterfactual and off-policy evaluation; production ML infrastructure, monitoring, and automated deployment. The systems you build should produce measurable economic lift in controlled experiments, generalize across businesses, learn from their own interventions, and safely automate an increasing share of real commercial decisions. the system should become better at operating the business because it has operated the business. We care more about exceptional technical ability and judgment than matching a checklist.



machine learning and statistical modeling; production ML systems; Python, SQL, and large behavioral datasets.

Most ML systems learn from a dataset. Here, the decisions made by the model influence the data the model sees next**. The long-term opportunity is to build that capability once and apply it across a portfolio of businesses and increasingly broad commercial decisions.

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📌 Senior Machine Learning Engineer, Causal & Decision Systems (Toronto)
🏢 CSC-Generatio
📍 Toronto