Top Data Jobs in Toronto

Build, improve, and deploy fraud and loss detection models using supervised and unsupervised ML, causal inference, optimization, and experimentation. Partner with engineering and operations to productionize models, run experiments, and surface insights to shape fraud strategy and reduce financial crime.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Build and maintain scalable data pipelines, dbt models, and warehouse infrastructure to deliver reliable data for analytics and BI. Collaborate with cross-functional teams to enforce data quality, documentation, and governance; monitor pipeline health, investigate and resolve issues, and support data migration and automation efforts.

Design, build, and maintain core data pipelines and models for Lyft's Safety and Customer Care platform. Scale processing, tune Spark/SQL/MapReduce jobs, implement data quality systems, enable self-service ETL tooling, and collaborate with product, engineering, and analytics teams to deliver reliable reporting and insights.

Lead a team to design, build, and operate large-scale data pipelines and warehouses, ensure data quality and SLAs, collaborate cross-functionally, leverage AI/LLM, and drive key data initiatives from planning to delivery.

Responsible for supporting data operations, generating reports, and maintaining data quality through advanced Excel and MS Access skills, along with SQL Server and C#. Opportunity for enhancing processes and CRM development.

Support the development and enforcement of data governance policies and standards. Collaborate with stakeholders to enhance data quality and compliance while managing data governance projects.

As a Senior Data Scientist, you will design AI solutions, lead NLP implementation, and mentor junior staff while transforming data into actionable insights.

Analyze complex datasets, build interactive dashboards, run predictive and statistical analyses, enforce data governance, automate reporting, and partner cross-functionally to deliver actionable insights.

Lead enterprise data, analytics, AI, automation, and information governance. Define strategy and roadmap, modernize data platforms (lakehouse/Azure Databricks), establish AI and automation capabilities, enforce governance/MDM (Semarchy), deliver BI/Power BI and predictive solutions, run Automation CoE, drive data literacy, and build a high-performing cross-functional team.

Design and own end-to-end data quality systems for managed AI data studies: define rubrics, sampling plans, automated checks, launch gates, drift detection, dashboards, and calibration. Investigate integrity issues, run root-cause analysis, build automation in Python/SQL, and partner with Product, Engineering, and Operations to embed quality controls, train reviewers, and scale repeatable quality playbooks across programs.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

Design, develop, and maintain scalable Databricks-based ETL/ELT data pipelines and transformations. Support migration from HANA to a modern lakehouse, ensure data quality and performance, troubleshoot issues, participate in code reviews, and collaborate with architects, analysts, and stakeholders to deliver enterprise reporting, analytics, and AI-ready data solutions.

Design, develop, and deploy machine learning and NLP solutions at scale. Work with cross-functional teams to build content classification, representation learning, search, and recommendation models using classical ML, deep learning, and LLMs. Process large datasets with Python, SQL, and Spark, and communicate results to stakeholders with clear documentation.

Plan and execute a zero-downtime migration of stateful data systems (Druid, Spark, Redpanda, Airflow, PostgreSQL, Elasticsearch) from bare-metal VMs to Kubernetes on OpenStack. Codify infrastructure as code, implement safe StatefulSet/PVC and upgrade patterns, and own day-to-day operational health. Build observability, SLOs, runbooks, KPIs, and optimizations. Partner with SRE on hardware, networking, and Kubernetes fundamentals while owning data applications end-to-end.

Lead design of scalable, governed end-to-end data solutions aligned to business priorities. Partner with product, engineering and architecture teams to define target-state patterns, integration, storage, consumption, controls and governance for reporting, operational and AI use cases. Provide technical leadership, review solutions, and ensure alignment with enterprise standards and platform strategy.

Lead presales engagements to design enterprise data platforms and governance on Microsoft Fabric and Azure Databricks. Conduct discovery workshops, architect governance with Purview/Unity Catalog, define AI-readiness and Copilot access, author proposals/RFP responses, run POCs/demos, and partner with sales and delivery teams to size and transition engagements to managed services.

Lead product vision and roadmap for Imogen Data, a platform that captures and transforms legacy system data. Collaborate with engineering, sales, marketing, and customer-facing teams to prioritize investments, drive execution, mitigate risk, and represent the product to stakeholders and customers. Evolve the product from MVP to enterprise scale while balancing strategy, delivery, and customer value.

Use diverse data sources to evaluate product performance, build automated ETL/data workflows, design dashboards and KPIs, own data product quality, and provide analytical insights to guide product and business decisions while establishing monitoring frameworks and best practices.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

Develop data-analysis pipelines bridging ultrasound, synthetic biology, imaging and sequencing. Collaborate with wet-lab and ML teams to define objectives, encode priors, run ML pipelines for de-novo design and closed-loop active learning, and produce actionable insights to accelerate molecule discovery and device optimization.

Responsible for designing and operating data architecture, building ETL/ELT pipelines, implementing data quality frameworks, and collaborating on ML initiatives.

Lead enterprise data strategy, governance, and analytics to turn data into actionable insights. Partner across IT, privacy, finance, operations and charity functions to implement data platforms, reporting tools, AI best practices, and data products. Build data literacy, mentor analytics staff, manage vendors, and support evidence-informed decision-making and impact measurement.

Lead enterprise data strategy, governance, platforms, analytics, and data product development. Advise leadership on AI, privacy, and data-driven decision-making. Partner across IT, Finance, Operations, and external vendors to deploy data solutions, build data literacy, mentor analytics staff, and measure organizational impact to inform investments and improve services for children and communities.

Lead strategy and research for Generative AI and Graph ML, drive end-to-end ML system design and production deployments, mentor scientists, align ML innovation with product goals, and represent the company externally.