Senior Manager, Data Science - ML Ops

Mississauga, ON

$109K - $150K/yr

Job Description

Position Summary...

This role defines strategy, requirements, and delivers scalable ML platforms, operational frameworks, and tooling that power Machine Learning and AI initiatives across the organization. The position collaborates across business and technology functions to ensure reliable model deployment, governance, and performance, while introducing innovative MLOps and supporting DevOps practices that strengthen automation and delivery pipelines.

What you'll do...

The role leads the strategy and execution of ML Operations, ML Platform, and Model Lifecycle Management functions, managing resource prioritization to maximize reliability, automation, and operational excellence. It integrates ML engineering, data science, and platform capabilities with business needs to deliver production-grade ML solutions with measurable impact. Collaborating with senior stakeholders in Operations, Merchandising, Finance, Technology, and Marketing, the role identifies opportunities where improved ML pipelines, platform tooling, or automation can enhance performance, reduce friction, or scale AI solutions enterprise-wide.
As a subject matter expert in ML systems and operational best practices, the role may oversee model deployment strategies, CI/CD pipelines for ML workflows, monitoring and observability frameworks, and automated retraining processes. It ensures that production models meet standards for performance, reliability, and governance. Additionally, the role builds and executes a long-term roadmap to elevate ML operational maturity by researching, piloting, and introducing innovative technologies in orchestration, feature engineering, model serving, and platform automation. Light DevOps responsibilities, which include infrastructure automation for ML workloads, containerization best practices, and collaboration with platform engineering, support this mission. The role also incorporates targeted data engineering activities such as enabling data ingestion, transformation, and movement into ML-ready environments to ensure that modeling pipelines are supplied with high‑quality, well‑structured data.
This role will partner with Data Science, Engineering, Architecture, and DevOps leaders across the organization to share best practices, operational standards, and platform technologies, collaborating to advance enterprise ML capabilities. Leading a cross-functional team of ML engineers, platform engineers, and other specialists, the role envisions, builds, tests, and delivers robust systems that enable impactful and reliable AI experiences at scale.

Qualifications – Must Have

Expertise in infrastructure automation to support scalable ML workloads.
Hands-on experience with container orchestration platforms, including Kubernetes.
Proven ability to design and manage multi-cloud deployments for ML systems.
Strong proficiency in ML lifecycle orchestration, including model training, deployment, monitoring, and retraining workflows.

Qualifications – Nice to Have