Top Data Jobs in Toronto

Design and analyze experiments, develop statistical and machine learning models, identify patterns in large datasets, build data pipelines, establish product health metrics, and translate insights into decisions. The role partners closely with product, engineering, operations, and other data science teams to improve Lyft payment experiences, payout features, earnings transparency, and platform integrity.

Develops data-driven solutions for credit cards and personal lending by extracting and preparing large datasets, applying statistics and advanced analytics, visualizing insights, and communicating recommendations to business stakeholders. Partners with data scientists, analysts, engineers, and developers to create scalable machine learning and data science solutions. Provides thought leadership, explains statistical methods, and supports knowledge sharing across the organization.

Designs and builds scalable data platforms and pipelines supporting AI products. Develops Spark, Python, Scala, Dask, and Argo workflows; creates data validation and performance tests; automates high-volume and real-time data delivery; operationalizes pipelines for data science and analytics; and optimizes cloud resource usage. The role also oversees other engineers and communicates technical solutions to clients and internal teams.

Own and improve operational metrics, dashboards, analytical datasets, data pipelines, and automated reporting for autonomous vehicle fleet operations. Analyze utilization, uptime, delays, downtime, readiness, mission performance, and commercial outcomes to identify root causes and improvement opportunities. Ensure data quality across systems, develop scalable workflows, and partner with Operations, Commercial, and Data Platform teams to deliver actionable insights and recommendations.

Build and operate Homebase’s data and ML platform, including CDC and streaming pipelines, Databricks infrastructure, Terraform deployments, real-time inference services, feature stores, and MLflow workflows. Partner with data science and engineering teams to productionize AI capabilities, improve reliability and observability, and participate in on-call operations. The role requires collaboration at the Toronto hub every Tuesday and Wednesday.

Leads and develops a data science team, mentors scientists, and oversees production machine learning models for product features and internal business systems. Designs experimentation programs, partners with Engineering, Product, Design, and executives, and drives applied AI initiatives across scheduling, hiring, payroll, pricing, personalization, sales, and marketing.

Design, develop, and maintain scalable cloud data solutions using Azure Data Factory, Azure Databricks, Power BI, Python, PySpark, and SAS. Build optimized data pipelines, apply data modeling principles, support data mesh architecture, and ensure data product quality through testing. Lead technical design, complex application development, technology roadmaps, code reviews, and mentoring while delivering enterprise-scale backend and mobile transformation projects.

Manages and participates in risk-based internal audits covering data, AI, third-party, project, and change risk. Evaluates control design and operating effectiveness, performs testing, documents audit work, validates remediation, communicates risk trends, and advises management on control and regulatory matters. The role requires strong audit reporting, stakeholder communication, risk assessment, and knowledge of relevant governance frameworks and certifications.

Develop and maintain enterprise Power BI dashboards, reports, scorecards, data models, and SQL queries. Translate stakeholder needs into scalable reporting solutions, define KPIs, support data governance and quality, validate reporting outputs, analyze performance trends, and maintain documentation. The role also supports project delivery, testing, release management, troubleshooting, and continuous improvement across operational, workforce, and strategic reporting.

Owns daily MDM operations, data supply, reconciliation, issue management, data quality measurement, and integrity controls across enterprise data domains. Leads root-cause analysis, remediation, data standards, lineage, governance, and data requirements for projects and change initiatives. The role drives automation and continuous improvement while reporting on data health, operational performance, ticket backlogs, and control effectiveness. It also requires stakeholder leadership, mentoring, and advanced SQL and Python skills in complex enterprise environments.

Leads the architecture, development, and maintenance of data pipelines, reporting frameworks, dashboards, and AI-enhanced analytics solutions. Analyzes complex datasets, delivers strategic insights to senior stakeholders, establishes data governance standards, mentors team members, and guides technical reporting initiatives from conception through deployment.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Designs, develops, deploys, and supports enterprise reporting and analytics solutions using Power BI. Builds scalable data and semantic models, transformation processes, visualizations, and reporting architectures. Translates business needs into actionable insights, improves data quality and reporting processes, supports production analytics, and provides stakeholder consulting and training. Develops self-service analytics capabilities, documentation, and user guides while diagnosing issues and coordinating technical resolutions.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Leads business analysis and governance activities across enterprise initiatives. Responsibilities include requirements gathering, process mapping, stakeholder workshops, portfolio intake, governance artifact maintenance, EDMO compliance, milestone monitoring, audit and compliance support, and continuous process improvement. The role partners with business, technology, and vendor teams to ensure initiatives meet governance, control, documentation, and reporting standards.

Senior Data Scientist supporting Canadian business units to design, develop, and deploy ML and GenAI solutions (including agents, chatbots, RAG, and vector-based retrieval). Translate business goals into analytics, perform feature engineering, model training/evaluation, prompt engineering, and mentor junior staff. Provide technical leadership, design artifacts, stakeholder communication, and drive continuous improvement.

Supports data strategy and analytics for clinical, operational, and real-world healthcare data in Roche Product Development. Responsibilities include cohort extraction, data transformation, observational analysis, reproducible pipeline development, visualization, quality control, and translating scientific requirements into analysis-ready datasets. Partners with study leads and data scientists on protocol feasibility and methodology, while contributing reusable code, documentation, and AI-assisted analytical workflows.

Develop and maintain large-scale data and machine learning applications using Scala, Python, Spark, AWS, and modern DevOps practices. Responsibilities include designing data pipelines, deploying and monitoring machine learning models, optimizing distributed systems, supporting the full software lifecycle, documenting processes, and collaborating with data science, Data Ops, DevOps, and product teams in an Agile environment.

Lead the architecture and delivery of GitLab’s data products, including near-real-time data ingestion, knowledge graph integrations, data marketplaces, observability, and data-sharing APIs. Translate ambiguous 0-to-1 initiatives into roadmaps, resolve cross-team dependencies, ensure operational readiness across deployment surfaces, and mentor engineers. The role requires deep full-stack expertise, technical leadership, and experience with AI-powered orchestration and multi-tenant SaaS systems.

Supports onboarding of international research sites by mapping records to a LIMS schema, migrating legacy data, executing bulk imports and scheduled transfers, reconciling identifiers and vocabularies, monitoring data feeds, resolving failures, verifying data integrity, and training partner-site users. The role uses SQL, PostgreSQL, Python, and PHP for data transformation and supports scientific drug-discovery data integration.

Owns the enterprise reporting and analytics function, including Power BI dashboard architecture, data governance, data integrity, executive reporting, trend analysis, forecasting, and decision support. Leads migration from legacy reporting tools, establishes a single source of truth, manages deliverable reporting, and supervises reporting analysts and document control staff. Partners with program and client leadership to ensure accurate, audit-ready reporting and actionable insights.

The Data Engineer will design, build, deploy, and maintain scalable data and software solutions across on-premise and cloud environments. Responsibilities include participating in the data engineering lifecycle, improving system performance and resilience, assessing cloud solutions, documenting architectures, collaborating with stakeholders and engineering teams, and implementing modern data practices using Azure, Databricks, Snowflake, Python, and SQL. The role also supports DevSecOps, CI/CD, infrastructure as code, and continuous improvement.

Provides Canadian privacy, AI, data protection, consumer protection, and regulatory legal advice for Mastercard products and services. Serves as Canadian Data Protection Officer, advises on product launches, privacy-by-design, data use, consent, cross-border transfers, impact assessments, contracts, regulatory readiness, and remediation. Monitors Canadian, U.S., and global developments and translates requirements into practical business guidance, controls, training, and stakeholder communications.

Designs and demonstrates secure, scalable Azure database and analytics solutions for enterprise customers. Leads technical sales engagements, proofs of concept, hackathons, architecture workshops, migrations, and production deployments. Advises platform leaders, resolves technical blockers, collaborates with engineering, and develops expertise across Microsoft Fabric, Azure Databases, Databricks, Purview, on-premises data warehouses, Hadoop, and competing analytics platforms.

Analyze player behavior, product performance, revenue, retention, and engagement for Zynga Poker. Design experiments, dashboards, ETL pipelines, telemetry, fraud models, and business intelligence solutions. Partner with product, game design, economy, and consumer insights teams to guide decisions and improve player experiences. Contribute to AI-enabled analytics workflows, game integrity initiatives, and analytics best practices.