Senior Analyst, Quant System

ia; Quebec; On-site; CAD 90,000 - 120,000; Full time

Job summary

iA Global Asset Management (iAGAM) seeks a Quant Systems Engineer to transform quantitative investment workflows into robust, production-ready solutions. You will work with Front Office teams to co-design and co-develop solutions for scalable analytics and reliable data pipelines.

The role emphasizes robust engineering practices, repeatable patterns, and evolving capabilities beyond a single use case, including AI-enabled enhancements and strong operational support.

Qualifications

• Proficiency with Python for quantitative workflows.
• Experience building data pipelines and analytics platforms.
• Strong collaboration with Front Office teams.

Responsibilities

• Partner with Front Office teams to co-design and co-develop quantitative solutions.
• Translate research and analytic workflows into production-ready implementations.
• Support the full lifecycle of quantitative solutions from design to deployment.

Job description

The Data Science function within iA Global Asset Management (iAGAM) is a key driver of strategic transformation across Investments, contributing to the organization's long-term vision and scalable systems and analytics objectives. The team works closely with Front Office investment teams to modernize analytical workflows, enable cloud-native solutions, and accelerate the adoption of advanced analytics and AI capabilities.

Within this mandate, Quant Systems focuses on transforming quantitative research and investment workflows into scalable, reusable, and supportable technology solutions.

Build the future with us

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.

What you’ll accomplish with us

Front Office Co-Development

• 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