Senior Quantitative Developer Jobs in Toronto, ON

The Quant Systems Engineer plays a critical role in enabling and scaling quantitative solutions used by investment teams. This role partners directly with Front Office investment teams to co-develop solutions while contributing to the robustness, standardization, and long-term supportability of the underlying analytics platform.

As a Quant Systems Engineer, you will help transform quantitative investment workflows into robust, reusable, and production-ready solutions. You will work closely with portfolio managers, quants, researchers, traders, and investment teams to understand analytical requirements, engineer scalable implementations, and ensure solutions can evolve beyond a single use case or team.

You will also help integrate modern AI capabilities and reliable data pipelines into quantitative workflows, ensuring that new capabilities are practical, observable, and aligned with investment use cases.

This is a hands-on technical role for someone who enjoys building high-quality software, working close to investment decision-making, and solving engineering challenges in a quantitative environment. While the role requires credible quantitative fluency, it is not intended to be a Front Office quant research role.

Partner with Front Office teams such as Quantitative Equity, Trading, Risk, Asset Allocation, and other investment teams to:

• Co-design and co-develop quantitative solutions supporting investment workflows.

• Translate research, investment, and analytical workflows into production-ready implementations.

• Act as a technical counterpart who understands both quantitative intent and platform constraints.

• Support the full lifecycle of quantitative solutions, from design and deployment through ongoing evolution.

• Bridge the gap between investment requirements and engineering implementation.

• Help teams standardize and operationalize analytical workflows.

• Collaborate with data engineering partners to ensure quantitative solutions are supported by reliable, validated, and well-orchestrated data pipelines.

• Identify opportunities to incorporate AI-assisted capabilities, automation, and intelligent workflow support where they can improve speed, quality, or decision support.

Reusable Capabilities & Engineering Standards

• Define, implement, and maintain reusable engineering capabilities, solution patterns, and development standards that enable quantitative solutions to scale across investment teams.

• Identify opportunities to generalize solutions across teams and investment functions.

• Contribute reusable Python packages, libraries, frameworks, and engineering practices that improve consistency across teams and environments.

• Ensure deployed solutions are maintainable, scalable, and supportable.

• Contribute to documentation, standards, and best practices for quantitative application development.

• Define reusable patterns for integrating modern AI capabilities into analytical and quantitative workflows, including responsible experimentation, validation, and operationalization.

Platform & Operational Contributions

• Improve the robustness of analytics and quantitative computing environments across production and non-production environments.

• Contribute to migrations, upgrades, and platform standardization initiatives.

• Build tooling, automations, and platform capabilities that directly support quantitative workflows.

• Participate in incident triage, root-cause analysis, and continuous improvement efforts.

• Help improve deployment, monitoring, troubleshooting, and operational support processes.

• Contribute to pipeline reliability through orchestration, observability, data quality checks, and clear operational runbooks.

• Design, build, and support data pipelines that connect source data, analytical transformations, model logic, and downstream reporting or application layers.

Examples of Work You May Contribute To

• Reusable Python packages and libraries for quantitative workflows.

• Standardized solution templates for research-to-production workflows.

• Orchestration patterns for recurring analytical or investment processes.

• Tools that improve deployment, monitoring, debugging, or supportability.

• Shared components for data access, modeling workflows, reporting, backtesting, simulation, or portfolio analytics.

• Migration patterns that help teams move existing workflows into modern analytics environments.

• Reusable data pipeline templates that standardize ingestion, transformation, validation, scheduling, and monitoring for quantitative workflows.

• AI-enhanced analytical workflows, such as intelligent summarization, assisted research workflows, forecasting support, or natural-language interfaces to curated data and analytics.

Technical Skills

• Strong Python software engineering skills, including production-grade development practices.

• Experience designing maintainable, modular, and reusable software solutions.

• Experience working in data-intensive, analytics-heavy, or quantitative environments.

• Familiarity with cloud-native development environments and shared tooling ecosystems.

• Working knowledge of Git-based development workflows, code reviews, and CI/CD concepts.

• Experience building internal tools, libraries, automation capabilities, or shared engineering components.

• Strong debugging and problem-solving skills across both research and production contexts.

• Ability to diagnose issues spanning application logic, orchestration, data dependencies, and runtime environments.

• Familiarity with modern AI capabilities, AI-assisted development practices, or applied AI/ML solutions in an enterprise setting.

• Experience designing or supporting ETL/ELT, orchestration, data validation, and data quality patterns for analytical or quantitative workflows.

Quantitative Literacy

Solid understanding of quantitative concepts such as:

• Time-series analysis.

• Financial instruments and risk concepts.

• Modeling, backtesting, and performance evaluation.

Ability to:

• Read and understand quantitative logic.

• Collaborate effectively with quants, researchers, and investment professionals.

• Translate quantitative requirements into scalable technical solutions.

• Balance engineering rigor with practical investment needs.

This role does not require being a Front Office quant researcher, but it does require credible quantitative fluency.

• Strong collaboration skills across investment, quant, data engineering, and platform teams.

• High ownership and ability to operate autonomously.

• Ability to manage ambiguity and drive technical initiatives to completion.

• Product-oriented mindset focused on maintainability, supportability, and reuse.

• Strong communication skills with both technical and non-technical audiences.

• Ability to balance short-term delivery requirements with long-term platform sustainability.

• Pragmatic engineering judgment and continuous improvement mindset.

• Undergraduate or master's degree in Computer Science, Engineering, Mathematics, Finance, Financial Engineering, or a related field preferred.

• 5+ years of relevant experience for intermediate candidates; 8+ years for senior candidates.

• Experience working at the intersection of software engineering, analytics, quantitative research, or investment workflows.

• Demonstrated ability to build and support production-grade technical solutions.

• Experience in quantitative finance, investment technology, trading, risk, or portfolio management environments is an asset.

• CFA, CQF, FRM, or other quantitative or financial designation is considered an asset.

• Experience working directly with Front Office or investment teams.

• Prior exposure to quantitative finance, trading, portfolio management, or risk environments.

• Experience supporting internal platforms, developer platforms, or shared services.

• Familiarity with modern orchestration, containerization, and automation frameworks.

• Exposure to cloud-native architectures and scalable application development.

• Experience contributing to platform engineering, developer enablement, or internal tooling initiatives.

• Experience with data pipeline orchestration, data quality automation, or analytical data product development.

• Experience integrating AI capabilities into production or near-production workflows with appropriate validation, controls, and monitoring.

• Intermediate proficiency in French, as the candidate will be required to communicate daily with French-speaking clients and partners across Canada via email and phone calls.