Top Data Jobs in Toronto

Lead development, training, deployment, and optimization of deep learning computer vision models using aerial and geospatial imagery. Build high-quality datasets, collaborate with product and engineering teams to translate requirements, and mentor data science team members while establishing best practices for production ML.

Lead Kraken's Data Platform team focused on real-time infrastructure for data systems, managing team dynamics, architecture design, and promoting AI automation.

The VP of Data & AI Engineering leads Duck Creek's enterprise data and AI strategy, overseeing global teams, implementing scalable platforms, and ensuring data governance and responsible AI practices.

Lead architecture and delivery of production-grade machine learning systems, mentor global ML engineers, drive model lifecycle (training, deployment, monitoring, retraining), and apply advanced ML (fraud detection, recommendation, churn/LTV) with MLOps and inference optimization.

The Software Engineer will provide technical support to customers, collaborate with engineering teams, and drive customer success initiatives while leveraging Python and SQL expertise.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

The Data Platform Marketing Manager will lead marketing strategies for DoiT's data lake and Snowflake optimization business, aiming to drive growth and impact.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Serve as the technical bridge for GTFS: drive specification change requests, support implementers, maintain documentation, facilitate community discussions, represent MobilityData at events, and provide technical guidance to internal teams to improve data quality and adoption.

Lead the Growth pod as the first dedicated data hire: build data infrastructure connecting event, product, GTM, and financial data; define metrics and run experiments with proper power analysis; build predictive models and reporting/automation; proactively surface insights and embed with Product, Engineering, and Design to drive activation, expansion, and retention. Bring applied AI to improve growth outcomes.

Design and implement data models, pipelines, and ML systems to power AI agents and analytics. Build, deploy, and monitor machine learning models at scale, integrate Snowflake/AWS and Salesforce Data Cloud, and deliver production-ready data products and views to stakeholders.

Research, evaluate, and manage partnerships with data providers. Assess datasets for coverage and interoperability, support deal structuring, maintain partnership pipeline, and collaborate with product, engineering, and legal to integrate data into the platform.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

Design business data models and data architectures, mentor data teams, translate business requirements into technical specifications, and set standards for AI-augmented development practices.

Own cloud operational health, architecture intelligence, and cost optimization. Analyze cloud spend, build dashboards/reporting, enforce governance, review architecture for cost impact, and translate technical findings for engineering, finance, and executives.

Design, build, deploy, and maintain production-grade measurement and ML systems (MMM, incrementality, insights, recommendations). Translate statistical and causal methods into scalable products, debug full-stack data and model issues, evaluate methodology tradeoffs, and collaborate with engineering, product, and customer teams while providing customer-facing technical support.

Lead end-to-end data and ML pipelines for onboarding and fraud decisions: ingest streaming and batch sources, build feature platform and entity resolution, productionize ML models, enforce PII/data governance, and set technical direction while mentoring engineers and ensuring low-latency, reliable serving.

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.

The Enterprise Solutions Manager drives operational excellence and client success through strategic analysis, process optimization, and project management, collaborating with various teams to deliver AI solutions.

Lead design and implementation of Jane's customer data infrastructure: own event collection, pipelines, CDP tooling, schema governance, identity resolution, and attribution. Work cross-functionally with marketing, BI, product, and engineering to set standards, evaluate tooling (e.g., RudderStack), ensure privacy-compliant instrumentation, and build reliable, consistent data delivery to the warehouse.

Manage technical data for IP Routing hardware NPI programs: support NPI from data entry through factory implementation, maintain product data and Engineering Change Orders across the product lifecycle, and coordinate with engineering, supply chain, and manufacturing to ensure accurate PLM/PDM records and implementation.

Lead enterprise-wide AI adoption by translating technical AI concepts for non-technical stakeholders, curating education and communications, partnering with change management and L&D, triaging requests, and building a team to drive measurable AI adoption across the organization.

Data Contributors will research startups, analyze global data for StartupBlink's Global Startup Ecosystem Index, and compile accurate information for publication.