Why is this role important? Please note Loblaw's Return to Office is 4 days in the office

Drive solutions: Design and implement applied machine learning/operations research approaches to solve business problems.
Collaborate with business teams: Work closely with Product, Store Operations, Fulfillment Specialist, and Store \& Area Managers to source data, establish requirements, and define success metrics.
Code robust solutions: Develop high-performance Machine Learning/Operations Research models using Python programming, leveraging massive structured and unstructured datasets from various sources.
Collaborate with Engineering and Data Platform teams: Work with these teams to build and deploy your Machine Learning/Operations Research models into production.
Design experiments: Conduct experiments to measure the effectiveness of your product in driving a seamless fulfillment experience through key metrics such as labor rate, pick efficiency, orders ready on time, fill rate, etc.
Document and share findings: Share results and findings with stakeholders in a structured manner and drive technical discussions with other Data Science teams.
MS or Ph.D. in a STEM field --- or BSC plus equivalent work experience in Data Science or a closely related role
Portfolio or strong interest in working in the domain of applied machine learning/operations research with a focus on predictive analytics, forecasting \& supply chain optimization
Ability to deal with ambiguity and take the lead in designing individual components within a larger technical solution architecture
Creative, resourceful, and productive problem-solver with a passion for tackling dynamic, open-ended problems
Comfortable working both independently and collaboratively
Proficient in Python programming
Experience with advanced SQL querying
Experience in shell scripting/Unix
Experience with optimization solvers such as Gurobi, CPLEX, OR Tools, Xpress
Familiarity with developing and implementing heuristic and metaheuristic algorithms for large-scale optimization problems
Familiarity with Large Language Models (LLMs) and agentic frameworks like Langchain
Experience with big-data tools, e.g., Spark, Beam, Kafka
Experience with Airflow for workflow scheduling
Experience with or working knowledge of a public cloud (AWS, GCP, Azure)
Knowledge of software development best practices

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Senior Machine Learning Engineer, Operations Research