Top Data Jobs in Toronto

Lead predictive modeling and causal inference initiatives for clients, developing models from exploratory analysis through production deployment, monitoring, and retraining. Apply GLMs, econometric methods, uplift modeling, propensity methods, and deep-learning forecasting. Process large-scale data with SQL and Spark, build models in Python, improve production reliability, and communicate findings to technical and business stakeholders in a consulting environment.

Conduct real-world data analyses using SAS or R and commercial claims and EHR databases. Build longitudinal study cohorts, define baseline and outcome variables, apply epidemiologic methods, perform statistical tests, regression and survival analyses, assess data quality, review protocols and statistical analysis plans, and program tables, figures, and listings. The role requires advanced biostatistics or epidemiology expertise and strong communication skills.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Analyze go-to-market performance across marketing, sales, revenue operations, and finance. Build reporting connecting campaigns to pipeline and revenue, investigate funnel and attribution questions, support budget analysis and planning, improve data quality, and communicate insights to technical and non-technical stakeholders. Collaborate with AI and Data Engineering teams on marketing data agents, architecture, and source tables.

Lead delivery of a large-scale Enterprise Data Platform transformation, manage project plans, risks, dependencies, and cross-functional teams; drive execution, stakeholder alignment, reporting, and adoption of modern data capabilities.

Design, build, and maintain the data warehouse and data lake, schemas, and reliable ETL/ELT pipelines to support AI agents and analytics. Implement data governance, quality checks, lineage, and access controls to meet ISO27001 and SOC2 Type 2. Partner with AI, InfoSec, and product teams to enable agent orchestration, integrate with Microsoft 365/Graph/OAuth, monitor platform health and costs, and support AI program planning and documentation.

Design, build and configure scalable Big Data applications and ingestion pipelines. Translate requirements into design and code, work with architects and business stakeholders, ensure solutions meet security and data privacy policies, and support CI/Agile SDLC practices while analyzing data stores and Hadoop cluster performance.

Design and own finance solution and data architecture for a multi-year finance transformation. Define integrations across sub-ledgers, GL, IFRS17 and actuarial systems, data flows, governance, controls, reconciliations, Snowflake/AWS data platform patterns, and reusable finance data products. Lead architecture governance, vendor assessments, and measurable outcomes while partnering with finance, actuarial, IT, and group architecture teams.

Design and build scalable data architectures and analytics-ready models. Create ER diagrams and manage schema lifecycle. Develop and optimize ETL/ELT pipelines using SSIS, ADF, Alteryx, and Power Automate. Work across SQL Server, Databricks, MySQL, and PostgreSQL. Translate OLTP sources into OLAP warehouses/marts, troubleshoot data quality and performance, and deliver client-facing consulting, architecture documentation, and technical solutions.

Lead end-to-end analytics and AI/ML initiatives: diagnose performance across customer/product/advisor dimensions, build scalable datasets, dashboards, segmentation, and predictive models, implement cloud-based ML solutions, produce executive-ready visualizations and stories, mentor teams, and drive adoption of advanced analytics across business stakeholders.

Lead enterprise data product strategy and roadmaps, partner with business and technology stakeholders to prioritize and deliver trusted data capabilities, ensure governance/security/quality, drive adoption and measure product success across global teams.

As a Data Analyst in People Analytics at Lyft, you'll manage data requests, build ThoughtSpot dashboards, and enhance insights using AI capabilities. You'll collaborate with HR to craft metrics and translate findings into actionable insights for leadership.

Lead design and delivery of enterprise-scale Azure data platforms and AI-powered automation. Build reusable frameworks, solve complex technical problems, mentor engineers, influence stakeholders, and champion data engineering best practices.

Build and deploy LLM agents, Python automations, data workflows, dashboards, and root-cause analysis tools. Analyze telecommunications data with SQL to improve network quality, reduce fraud and billing errors, and optimize margins. Establish data architecture, governance, KPIs, and safe AI orchestration frameworks. Partner cross-functionally with Carrier Relations, Technology, Product, Supply Operations, and Finance to deliver scalable data products and communicate actionable insights.

The Senior Manager of Data Engineering leads a team in developing data and AI products, guiding architecture and design, and ensuring process improvements while managing stakeholder expectations and technical implementation.

Lead and manage clinical data management projects end-to-end: delegate tasks, oversee database build/migration, ensure data quality and standards, manage budgets/risks, conduct audits, liaise with stakeholders and vendors, and support DM deliverables including cleaning, reconciliation, and eTMF documentation.

Lead and grow the Data Governance function, operating the data catalog (DataHub), defining data quality frameworks and SLAs, managing access and classification, running audits, enabling domain stewardship, aligning compliance with Legal and Security, and building cross-functional governance programs and tooling integrations to enforce data quality and entitlement policies.

The Senior Data Strategist will manage customer data governance, define strategies, and collaborate across departments to improve data quality and usability.

The Senior Software Developer will design and build scalable APIs and distributed systems, improve operational efficiency, and mentor other engineers, focusing on automation and data infrastructure.

The Vice President, Data Scientist will develop and implement machine learning models to enhance algorithmic trading strategies and provide data-driven insights for Investment Banking and Corporate Banking.

Build and maintain Geotab's internal ML and generative AI platform, including model management, logging/monitoring, and alerting. Enrich large-scale telematics data, integrate data pipelines with data scientists, and support internal teams with infrastructure, documentation, and production GenAI applications and agents.

Support cross-functional teams by translating business questions into data needs, extracting and integrating datasets, ensuring data quality, conducting exploratory and advanced analysis, and building dashboards and visualizations (Tableau, Excel). Partner with data engineers and scientists, source external data, apply segmentation and spatial analysis, and present actionable recommendations to drive strategic decisions and product/marketing initiatives.

Lead evolution of Allied's Microsoft-centric enterprise data platform (Fabric, Power Platform, Power BI), integrate Yardi and IoT sources, build Power Apps and Power BI solutions, advance data governance and modelling, enable AI/LLM integrations, and drive reporting modernization and stakeholder adoption across the business.

Design, build, and maintain ETL/ELT pipelines and data models to ingest and process customer/contact data. Implement SQL-based transformations, data quality rules, validation, monitoring, and reconciliation. Optimize performance and storage, perform root-cause analysis and remediation, support QA/UAT, and drive automation and AI-assisted workflows to maintain trusted golden records.

Design, build, optimize, and monitor scalable data products, pipelines, APIs and analytics solutions on Azure. Mentor junior engineers, implement CI/CD and infrastructure-as-code, ensure security and performance, and partner with stakeholders to deliver production-ready data for analytics and automation.