Top Data Jobs in Toronto

Lead digital transformation initiatives, enhance commercial strategy, manage technology portfolios, and drive operational excellence in marketing and sales.

The Director, People Business Partner will enhance organizational effectiveness, talent management, leadership support, and people strategies using AI and data to improve outcomes and performance.

The Senior Marketing Analytics Lead will oversee analytics projects, translate data into insights, influence marketing strategies, and mentor junior team members.

As a Product Manager, you'll lead product vision and strategy, utilizing customer insights and technology to create innovative solutions while collaborating with multiple teams.

Lead advanced quantitative analysis of primary market research using methods such as Conjoint, MaxDiff, segmentation, and regression. Design experiments, validate models, interpret results, advise on methodology, partner with cross-functional teams, mentor junior analysts, and help build analytics standards and tools to support multi-market consulting engagements.

The Data Engineer will develop and maintain data pipelines, ensure data quality, and collaborate with business teams to implement insightful data solutions.

Lead R&D on ML solutions to detect risks in agentic commerce, prototype models, engineer features, run experiments and validations, and deploy scalable data science systems using Databricks, Spark, and cloud platforms while collaborating with product and engineering teams.

Lead a data science team to design and deliver merchant registry and profiling ML capabilities for onboarding and monitoring. Define strategy, guide model development and validation, promote reusable data assets, partner with Product and Engineering, drive Agile delivery, hire and mentor team members, and communicate impact to senior leadership to scale production ML systems on modern cloud/data platforms.

Lead global portfolio marketing for Advisors & Transformation, defining positioning and go-to-market for AI-enabled consulting (including Agent Suite). Drive integrated campaigns, sales enablement, thought leadership, and data-driven insights to accelerate adoption, pipeline, and revenue growth while aligning product, consulting, and regional teams.

Lead design and build scalable data architectures, ETL pipelines, ingestion and data quality frameworks. Collaborate with cross-functional teams, enforce data governance, analyze data for insights, mentor junior engineers, and serve as the technical authority on data engineering best practices.

Design and deliver analytics platforms connecting AI cost governance and ITSM operational intelligence. Build integrated data pipelines and dashboards (executive and operational), identify AI enablement opportunities, measure productivity impact of AI, and define scalable analytics architecture and governance across Snowflake, Microsoft Fabric, LiteLLM, and ServiceNow.

Design, build, and maintain scalable data pipelines and storage (data lakes/warehouses). Integrate diverse sources, enforce data quality, security and compliance, optimize ETL/ELT, monitor pipelines, and mentor junior engineers while collaborating with data scientists, analysts, and stakeholders.

Lead development and production deployment of AML machine learning models on large-scale payments data. Own end-to-end problem framing, prototyping, model performance, explainability, and scalability. Collaborate with engineering and product teams to deploy and maintain models, produce clear documentation, and ensure regulatory, privacy, and security compliance.

Design and maintain Azure DevOps CI/CD pipelines and automate deployment of Microsoft Fabric, Azure Data Factory, Synapse, Databricks, and Power BI. Implement IaC (Bicep/ARM), manage Azure environments, security, secrets, monitoring and logging, and collaborate with cross-functional teams to improve automation and platform delivery.

The Data Scientist will design and build machine learning solutions, optimize for business value, and guide customer success through technical expertise and collaboration.

Serve as the Data Cloud SME for Canada, partnering with Sales and Customer Success to deliver analyses, client demos, tailored data strategies, manage regional requests, support partnerships and RFPs, and enable internal teams to drive adoption and measurable outcomes.

The role focuses on extracting, cleaning data, building and maintaining machine learning models, and optimizing business strategies using data science methodologies.

This role involves collaborating with clients to define use cases, implement data integrations, improve ML architecture, and ensure customer success in using BrazeAI.

The Engineering Manager leads Data Engineering teams, developing scalable data solutions and collaborating with stakeholders to drive McCain's digital transformation.

Lead a small team and contribute hands-on to design, develop, deploy, and iterate AML and crypto risk detection machine learning models. Own technical roadmap, collaborate with Product and Engineering, ensure model quality, explainability, documentation, and governance, and drive production performance and outcomes across the model lifecycle.

Support transaction and sanctions monitoring programs by tuning alerts, designing analytical features, and optimizing data analysis processes to prevent financial crime.

Lead Line 2 information risk oversight for technology, cyber, data, AI, cloud, and emerging tech. Coordinate regulatory responses, review evidence packages, perform risk assessments, challenge Line 1 controls, track remediation, and produce risk reporting and governance materials for senior leadership and regulatory readiness.

CommBank is seeking Indigenous job seekers for various roles across its sectors, offering traineeships, internships, and direct employment opportunities with support from an Indigenous Careers team.

Lead client-facing data solutioning and pre-sales within Consumer verticals, design and translate architectures into delivery, maintain ~50% billable involvement, drive pipeline growth and close complex data opportunities while ensuring information security and privacy compliance.

Join the Cloud Data Ingestion & Transformation team to design, build, and operate scalable data ingestion and transformation systems. Own end-to-end features (primarily back-end with front-end delivery), process very high data volumes, drive architectural decisions, and improve platform reliability and customer data integrations.