Top Data Jobs in Toronto

Design, build, and govern Float's dashboards, metrics, and self-serve BI. Translate business questions into durable data products, validate dbt models, reduce ad-hoc requests, and use AI to scale analysis and accessibility for Finance, RevOps, GTM, and leadership.

Serves as a strategic HR advisor to Global Data and Business Enablement executives within TD Securities. Develops workforce plans, succession strategies, talent and organizational design initiatives, and change-management programs. Partners with Talent Acquisition, Legal, Compliance, and HR Centers of Excellence to support hiring, engagement, retention, regulatory compliance, and organizational effectiveness. Uses HR metrics and analytics to inform decisions and influences senior stakeholders across a global, matrixed financial-services environment.

Lead end-to-end machine learning and scorecard projects for origination, credit limit and collections. Design, develop, validate, deploy and monitor predictive models and segmentations. Collaborate with business partners, interpret results, address system issues, produce reports, and present findings to senior management while supporting related ad hoc tasks.

Design, deliver, and maintain core data repositories and documentation for investment-domain solutions. Perform data profiling, validation, and cleansing; define data models, interfaces, flows, and transformation rules; partner with Data Governance and QA to establish standards, templates, and testing; advise project teams and provide peer reviews to ensure solutions meet business needs.

Lead end-to-end machine learning and scorecard projects for origination, credit limit and collections. Own data sourcing, model development, validation, deployment, scoring operations and reporting. Produce customer segmentation, evaluate predictive performance, collaborate with business partners, and present results to senior management while supporting ad hoc analytics needs.

Design, build, operate and improve data pipelines, lakehouse assets and cloud infrastructure (Microsoft Fabric/Azure). Ingest, transform, validate and deliver data from enterprise systems; support observability, troubleshooting, VMs, Docker, source control, and cross-functional product squad collaboration to deliver analytics and application data products.

The Associate Director of Data Sciences drives SOTI's AI and data strategy, develops AI capabilities, and mentors teams to enhance product efficiencies.

The Senior Data Scientist will collaborate on AI projects, develop scalable code, ensure software quality, and guide product research and development.

Analyze sales, pricing, margin, customer, and operational data to produce actionable commercial insights. Build and maintain Power BI, Tableau, and Excel dashboards, automate reporting, track KPIs, support forecasting and demand planning, and identify trends and profitability opportunities. Partner with Sales, Marketing, Finance, Operations, and IT to improve data quality, CRM and ERP integrity, governance, and reporting processes while communicating findings to nontechnical stakeholders.

Leads data architecture, integrations, product analytics, and client-specific analyses for an AI-enabled life sciences platform. Partners with engineering, product, solutions, and client services teams to build scalable, AI-ready data systems and improve measurement insights. Manages and mentors data analysts and engineers while supporting pharmaceutical and life sciences customer needs.

The Data Scientist II will use marketing analytics, multi-touch attribution, media mix modeling, and causal inference to measure campaign performance and improve customer personalization. Responsibilities include designing tests, analyzing marketing channels, delivering actionable insights, advising stakeholders, managing project timelines, and supporting risk management and data controls. The role requires collaboration across marketing and business teams, strong communication, creative problem-solving, and expertise with customer data platforms, analytics tools, data warehouses, and business intelligence technologies.

Leads end-to-end analytics initiatives, partnering with clients and stakeholders to define metrics, analyze performance, design dashboards, evaluate experiments, and deliver actionable recommendations. Collaborates with data engineers on data models and reporting quality, establishes analytics standards and governance practices, mentors junior analysts, and presents insights to leadership through clear narratives and visualizations.

Support client analytics initiatives through exploratory analysis, dashboarding, KPI tracking, recurring reporting, data quality validation, and insight communication. Translate business questions into analytical requirements, document metrics and processes, collaborate on data modeling and reporting layers, and identify workflow improvements. The role also provides exposure to digital consulting, product development, user experience, and data governance.

Leads enterprise AI and data security strategy, roadmaps, standards, requirements, governance, and prioritization. Partners with architecture, engineering, platform, risk, and business teams to embed security controls across AI systems, data platforms, databases, and encryption technologies. Develops capability frameworks, heatmaps, and prioritized recommendations; manages dependencies and presents plans to executives. Requires deep expertise in AI security, data protection, governance, privacy compliance, and cybersecurity program management, along with strong stakeholder influence and relevant industry certification.

Maintain data accuracy, consistency, and governance across Yardi and other enterprise systems. Responsibilities include Yardi data setup and configuration, ServiceNow ticket administration, SAP Concur support, release testing, system enhancements, user support, requirements coordination, and process improvement. The role collaborates with Finance, Property Accounting, Lease Administration, Operations, Technology, and other stakeholders to strengthen enterprise data quality and operational effectiveness.

Analyzes operational data to identify trends, risks, bottlenecks, and improvement opportunities. Builds and maintains Power BI dashboards, scorecards, and scalable reporting solutions covering performance, efficiency, quality, service levels, and capacity. Partners with Operations, Process Improvement, and Technology teams to improve data quality and usability, establish reporting governance, and provide executive-ready insights and recommendations that support operational decision-making.

Analyze credit risk and portfolio data, prepare recurring and ad hoc reports, support analytics automation, contribute to data lifecycle activities, document requirements, and explore generative AI tools for risk-management insights.

Designs and maintains enterprise data models for property and casualty insurance domains, including policy, claims, underwriting, risk, regulatory, and premium data. Translates business requirements into conceptual, logical, physical, dimensional, and canonical models; creates ERDs, dictionaries, metadata, and data flow diagrams; and supports data governance, quality, lineage, compliance, migration, cloud adoption, and system modernization. Collaborates with stakeholders, architects, engineers, and governance teams to validate scalability, integration, performance, and alignment with enterprise standards.

Designs and develops performance measurement datasets, reporting solutions, and analytics applications for Finance. Responsibilities include requirements analysis, solution design, coding, unit testing, data quality support, incident resolution, and documentation. Partners with business and technology teams to clarify needs and maintain reliable data processes. Requires advanced SAS and Oracle SQL, stored procedure and ETL development, database object creation, and automated processing. Hybrid role based in Toronto.

Design and deliver data analytics and AI solutions for Capital Markets. Apply statistical and machine learning models, build AI agents and dashboards, produce insights for senior management, and collaborate with data engineers and business teams to ensure governance and integration.

Design, develop, and optimize Azure SQL databases, data warehouses, ETL pipelines, and lakehouse solutions for datasets exceeding 5TB. Write complex SQL, stored procedures, functions, and triggers; perform performance tuning and troubleshooting; and develop data models and cloud infrastructure solutions. Lead technical design, roadmaps, code reviews, and complex backend projects while mentoring developers. Work with Azure Data Factory, Databricks, PySpark, Delta tables, and Agentic AI tools.

Leads enterprise client data strategy, integration, governance, quality, analytics, automation, and technology transformation for Group Benefits and Group Retirement Services. Manages approximately 40 professionals, establishes performance metrics, modernizes data exchange platforms, and partners with business units, clients, vendors, and technology teams. The role ensures secure, compliant, scalable data solutions while using operational analytics to improve efficiency and client outcomes.

Leads the development and deployment of advanced analytics, data science, and AI solutions for supply chain management. Partners with business, finance, technology, ERP, and vendor teams to create analytical products, dashboards, and decision-support tools. Applies machine learning, optimization, NLP, and statistical modeling to improve forecasting, inventory, supplier, procurement, contract, and spend performance. Establishes data governance, model monitoring, quality practices, and responsible AI standards while mentoring collaborators and promoting data-driven decision-making.

Analyzes business needs and translates them into system, data, reporting, and process solutions. Responsibilities include requirements gathering, process mapping, ERP support, application configuration and testing, vendor coordination, UAT, dashboards, operational reporting, KPI development, IT support, cybersecurity assistance, and staff training. The role requires collaboration across business and technical teams in a regulated environment and is a 12–18 month, five-days-per-week on-site maternity leave coverage position.

Build scalable GCP-based data pipelines and datasets for insurance fraud analytics. Develop and evaluate machine learning, NLP, and Generative AI models using claims and unstructured SIU data. Apply explainable AI techniques, conduct exploratory analysis, automate data workflows, document architectures, and support privacy, fairness, and compliance requirements while collaborating with investigators and business analysts.