Top Python Jobs in Toronto

Analyze distribution center processes and performance using Lean Six Sigma, time-and-motion studies, SQL and Excel. Create process maps, support forecasting and simulation, lead improvement projects, and present recommendations to operational and leadership stakeholders.

Design, build, and maintain secure, high-performance back-end solutions in an Agile team. Provide technical leadership, design and review architectures, implement CI/CD deployments, use Databricks/PySpark and Azure data services, and improve performance monitoring and reliability.

Build and own end-to-end product capabilities across frontend and backend systems. Implement responsive React + TypeScript UIs, design and operate APIs and backend services (Python/TypeScript), deploy and run containerized apps on AWS/Kubernetes, integrate analytical data sources, use observability and CI/CD practices, and collaborate with product, design, and ML teams to ship reliable, incremental features.

Build and run web-scale data pipelines and crawlers to surface AI-generated content and hallucinations. Use detection models and LLMs to rank and classify findings, synthesize results into compelling prose, visuals, and web displays, and improve models based on investigation outcomes. Automate monitoring of social feeds and support investigative reporting workflows.

Build, operate, and improve a scalable asynchronous platform: monitor system metrics, design and deploy tooling for reliability and efficiency, automate infrastructure (IaC), participate in design and production readiness reviews, capacity planning, document operations, and join on-call rotations to respond to incidents and mitigate customer impact.

Lead definition and execution of a company-wide experimentation and measurement platform. Build and scale centralized experimentation infrastructure (Eppo), integrate with the data ecosystem, establish governance, tooling, lifecycle management, measurement standards, and partner with Product, Data Science, Analytics, and Strategy to drive rigorous experiment-driven decision-making.

Perform actuarial analyses, research, validations, and financial projections for P&C insurance. Develop monitoring reports, dashboards, and KPIs; collaborate with claims, legal, and operations; document actuarial conclusions; lead moderately complex projects; provide subject-matter expertise, mentorship, and process improvements while ensuring regulatory and risk control compliance.

Develop and implement quantitative methodologies to measure physical climate risks (flood, wildfire, hurricane, extreme heat). Translate hazard and damage estimates into portfolio and credit risk metrics (PD, LGD, ECL). Integrate and evaluate third-party catastrophe models, build scalable Databricks/Python/R/SAS workflows, produce reporting for stakeholders, maintain model documentation, support model validation and regulatory reporting, and coordinate cross-functional delivery.

Lead analytics for Digital Channels, delivering end-to-end analytic solutions, curating multi-source data, building automated Power BI visualizations, applying descriptive and predictive models, and advising product teams to inform omni-channel strategy and decision-making.

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.

Build and maintain Power BI semantic models and Microsoft Fabric data pipelines from Salesforce and GTM systems. Deliver analyses that inform GTM decisions, reconcile data across systems, own metric definitions, and document lineage and runbooks. Provide ad hoc investigations and present findings to leaders.

Design, build, and maintain scalable cloud data pipelines and architectures. Develop and optimize ETL/ELT workflows, manage data warehouses (primarily BigQuery), implement governance and security, collaborate with engineers and analytics teams, enable ML/AI use cases, and maintain documentation, lineage, and high-availability pipelines.

Plan, execute, and analyze performance tests (load, stress, endurance, scalability) for SOTI products. Monitor system metrics, identify bottlenecks, report findings, collaborate with dev/QA/DevOps, support networking and VM/environment administration, and automate repetitive tasks via scripting.

Serve as liaison between business and technology for Capital Markets client data initiatives: elicit and document requirements, write user stories, design solutions, create and execute test plans (automated and manual/SIT), coordinate UAT, analyze issues, and collaborate with technical teams to ensure successful delivery within an Agile 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.

Work directly with enterprise customers to design, implement, and scale data intake and workflow solutions on the Feathery platform. Own technical delivery from discovery to production, build prototypes and demos, advise customers on best practices, create implementation documentation, and help scale repeatable playbooks while partnering cross-functionally with Sales, Product, and Engineering.

Early-career business analyst on a multi-year investment platform transformation. Support requirements gathering, hands-on data analysis, testing, and documentation across conversion, data integration, data platform (Snowflake) and custom solutions. Engage with stakeholders, run test plans, investigate data discrepancies, and contribute to AI-driven process improvements and change management.

Build and operate backend-heavy full-stack applications, APIs, dashboards, and data pipelines. Integrate ML/AI models into production workflows, design data models, containerize and deploy services with Docker/Kubernetes, improve reliability and observability, and collaborate with research, data science, product, and design teams.

Provide hands-on support and maintenance for a cloud-native data platform (Azure Databricks, ADLS Gen2, Delta Lake, Azure SQL, MongoDB). Support Spark-based ingestion, transformations, migrations from Hadoop/HBase, CI/CD, observability, performance tuning, and production incident resolution. Collaborate with stakeholders, lead design/code reviews, promote DevOps and engineering best practices, and ensure secure, scalable, production-ready analytics solutions.

Build and harden scalable backend APIs and services using Python, Django, and FastAPI. Work with LLMs in production—creating pipelines, evaluations, and prompting strategies—while implementing observability and optimizing data systems. Collaborate across product, frontend, and data teams, participate in code reviews, and own complex technical problems to deliver reliable, audit-ready financial data products.

Design, build, and maintain production LLM integrations and agentic workflows. Implement RAG pipelines, context-window strategies, guardrails, evaluation frameworks, and monitoring. Collaborate with cross-functional teams to productionize reliable, compliant AI features and upskill engineers in context engineering practices.

The role involves designing and integrating intelligent translation workflows using machine translation, generative AI, and prompt engineering, ensuring compliance and performance optimization.

Build and maintain core financial backend systems (trading, ledger, billing), transaction processing, reconciliation, and portfolio state management. Design and scale real-time and batch data pipelines, architect APIs and backend services, ensure reliability and observability, and contribute to distributed system design (event-driven, microservices, streaming). Partner with ML teams as needed.

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.

Own QA for web and mobile releases: write and run manual and automated tests (web, mobile, API), build test frameworks, improve CI, track issues, mentor teammates, and influence requirements and quality processes.