Top Data Science Jobs in Toronto

As an Associate Product Manager at Switch, you will drive customer success, manage software implementation, and collaborate with engineers while shaping the product roadmap based on customer feedback and analytics.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Leads MAU analytics and data science, managing decision scientists and data scientists while setting quality standards. Owns MAU driver modeling, forecasting, causal analysis, experimentation, and exploratory work supporting growth strategy. Establishes consistent analytical practices, partners with revenue, product, engineering, and data engineering leaders, and embeds the team in roadmap planning. Promotes AI-native workflows through coding agents, automation, and analytical tools while coaching team members into strategic advisors.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

The Engineering Leader will oversee software and data teams, ensuring product delivery and operational excellence in robotics and IoT systems.

Lead the development and delivery of AI-driven product capabilities, transforming data into actionable insights while mentoring a high-performing team of data scientists and engineers.

Evaluate and label model-generated and human-written code outputs, debug and assess code/agent trajectories, prompt models for complex data science tasks, review visualizations, and report quality trends to improve model behavior.

Design and maintain ETL/ELT pipelines and semantic/analytical data models; extend metric layers for BI and LLM/RAG-driven natural-language analytics; apply AI/ML and data-science techniques to produce trusted investment metrics, insights, and recommendations; embed data quality, lineage, and governance; collaborate with investment, finance, and technology stakeholders to prototype and productionize AI-enabled reporting solutions.