Top Data Jobs in Toronto

Lead retail and loyalty analytics by designing measurement frameworks, causal inference and experimental designs, building statistical, ML, and deep learning models, developing agentic AI and LLM solutions, and orchestrating scalable end-to-end analytical pipelines to improve customer engagement and marketing performance.

Lead retail and loyalty analytics by designing measurement frameworks, causal inference and experimental designs, building statistical, ML and deep learning models, and developing agentic AI solutions. Drive optimization and orchestration of end-to-end analytical pipelines, produce actionable insights for marketing and customer engagement, and present findings to business stakeholders.

Develop and scale customer-facing analytics, dashboards, reports, and data products for a healthcare AI platform. Partner with Product, Engineering, Sales, BizOps, customers, and other stakeholders to translate needs into data requirements and actionable insights. Lead reporting initiatives, resolve customer reporting issues, support high-stakes customer interactions, improve internal workflows, and help productize operational and clinical data into differentiated customer value.

Design and optimize data pipelines, cleanse and transform datasets, and build reliable reporting and analytics solutions using PostgreSQL, Metabase, and related tools. Collaborate with stakeholders to define KPIs, develop dashboards, troubleshoot data processes, and deliver actionable insights that support data-driven decisions.

The Database Research Expert will lead the architecture design for a data analytics platform, optimize database performance, and guide research on innovative database technologies.

The role involves analyzing software requirements, contributing to software development, mentoring developers, and leading design/code activities in a big data context.

Research emerging trends in AI-powered big data systems, develop proofs-of-concept, optimize performance, and collaborate on innovative solutions.

The Technical VP for AI & Data Storage will define technology roadmaps, lead research advancements, and mentor engineers in evolving storage systems integrated with AI.

The Technical VP will lead innovations in big data and AI strategies, optimize data performance, manage R&D teams, and engage with industry communities.

Conduct applied research to develop AI data engine and storage architectures for large-scale workloads, optimize system software, and advance AI storage technologies.

The Associate Director will lead complex data centre projects, manage client relationships, mentor teams, and oversee project governance.

The Manager of Data Operations will lead a team to ensure the reliability and scalability of data platforms, enforce operational standards, and implement CI/CD practices. Responsibilities include monitoring data quality, incident management, and collaborating with various teams to enhance data operations.

Lead and grow a Data Engineering team to build production data pipelines, medallion datasets, and reusable data products. Drive architecture, engineering standards, design reviews, and cross-functional delivery. Partner with Product, Analytics, Data Science, and Platform to enable governed, reliable data foundations for analytics and AI. Mentor engineers and manage multiple concurrent initiatives to deliver predictable outcomes.

The role involves developing innovative machine learning solutions, transforming datasets into insights, maintaining data tools, and collaborating across departments to promote data-driven decision-making.

The Principal Data Engineer at RAVL will design and implement enterprise-grade data and AI platforms, mentor engineers, and influence architectural standards across teams.

The Data Engineer will design and manage data pipelines and architectures, ensuring data quality and security while collaborating with cross-functional teams for effective analytics and reporting.

Develop and implement AI risk assessment and pricing frameworks by evaluating AI models, creating rating tables and pricing models, collaborating with underwriters, building Python-based analytical tools, and researching emerging AI risks and mitigation strategies.

The role requires developing solutions using IBM DataPower, with a focus on creating WSP, XFW, MPGW, and managing certificates and encryption. Candidates should have strong skills in troubleshooting and working with various DataPower devices.

Lead design and implementation of a federated Lakehouse data platform, unify data silos, define governance/observability, enable secure AI/ML (RAG/vector readiness), mentor architects/engineers, and present roadmaps to executives.

The Data Engineer is responsible for designing and developing cloud-native data pipelines, integrating machine learning models, and ensuring the reliability and scalability of data solutions in a collaborative environment.

The Data Engineer will analyze and interpret data to drive insights, create dashboards, and enhance product performance, collaborating with cross-functional teams.

As a Data Analyst, you will analyze user behavior and product performance, creating reports and dashboards to aid decision-making and optimize user experience.

The Product Analyst will oversee product analytics, collaborate with teams, define metrics, build dashboards, and improve data quality in a hybrid work environment.

Own Zip's account universe, territory carving methodology, and enrichment strategy. Manage data quality across Salesforce, run annual data refreshes with vendors, build GTM analytics, automate workflows with AI, and partner with sales and Business Systems to execute territory and routing changes.

Senior Business Data Scientist, Marketing partners with cross-functional leaders to drive customer analytics across acquisition, onboarding, engagement, retention, monetization, and expansion. Lead deep-dive analyses, experimentation and impact evaluation, KPI governance, forecasting, and predictive analytics. Translate complex business questions into actionable recommendations, build reusable decision frameworks and scalable analytics assets, mentor analysts, and communicate executive-ready insights to influence strategic priorities and improve long-term customer value.