Top Data Jobs in Toronto

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.

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.

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.

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

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

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

The role involves defining and managing the product vision and strategy for an internal data lake, collaborating across functions to enhance data governance and quality, and engaging with senior stakeholders on product development and prioritization.

The Data Center Technician is responsible for installing, maintaining, and troubleshooting optical transport in data centers, ensuring quality and customer satisfaction while managing technical documentation and audits.

Develop ML/AI systems and analytics to detect fraud and cyber attacks using transaction-level data. Collaborate with intelligence teams to identify breach techniques, build global detection tools, automate transaction risk decisions, conduct experiments, and communicate findings.

Design and develop scalable data science capabilities to assess merchant risk, including feature engineering, entity resolution, prototyping ML solutions, building Databricks/Spark workflows, validating and monitoring models, and collaborating with Product and Engineering in an Agile environment.

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 the design and development of machine learning models, collaborating with teams to optimize solutions and mentor junior members while ensuring technical excellence.

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.

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 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.

The DCIM Administrator manages data center operations by overseeing Nlyte workflows, ensuring accuracy in asset management, and driving continuous improvement in process and data integrity.

Lead the Data Science team to implement advanced ML models in AdTech, optimizing bidding strategies and enhancing decision-making through Deep Learning architectures while managing team performance and collaboration.

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.

The Data Engineer will design and maintain ETL/ELT pipelines using various tools, support data infrastructure in cloud environments, and collaborate with BI teams for reporting and analytics.

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

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Lead a data science team to design, train, evaluate, and implement ML models using Python, AWS, and agentic AI. Partner with cross-functional stakeholders, translate technical work to business outcomes, manage talent, and evaluate new technologies for digital banking risk and decision-making.

The Senior Data Engineer will develop and maintain data platforms on Google Cloud, optimize data pipelines, and support Data Science teams in model training and analytics, collaborating closely across departments.

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

As a Product Manager for the Experimentation Platform, you will craft strategy, manage the roadmap, drive user adoption, and partner across teams for improvement using data-driven insights.