Senior Analyst, Quant System

iA Financial Group (Industrial Alliance); Toronto; Hybrid; CAD 70,000 - 110,000; Full time

Benefits offered by this job

Flexible group insurance

Stock purchase plan

Competitive pension plan

Wellness/personal development days

Discounts on iA products

Job summary

iA Global Asset Management (iAGAM) seeks a results-driven quant software engineer to join the Front Office Co-Development effort. You will partner with Quant teams to translate research into production-ready solutions and to build reusable, scalable components.

The role emphasizes cloud-native architectures, data pipelines, AI-assisted workflows, and platform engineering standards. You’ll contribute to tooling, orchestration, observability, and validation to raise analytics quality and speed

Qualifications

• 5+ years of relevant experience in software engineering, analytics, or investment technology.
• Strong Python software engineering skills and production-grade development practices.
• Experience with data pipelines, ETL/ELT, and data quality.
• Familiarity with cloud-native environments, Git, and CI/CD.
• Able to translate research into scalable production systems.

Responsibilities

• Co-design and co-develop quantitative solutions for investment workflows.
• Translate research workflows into production-ready implementations.
• Collaborate with data engineering to ensure reliable data pipelines.
• Define reusable patterns and contribute to engineering standards.
• Improve deployment, monitoring, and operational runbooks.

Skills

Python

Software engineering

Cloud-native

Git & CI/CD

Data pipelines

AI/ML familiarity

Education

Undergraduate or master’s degree in Computer Science/Engineering/Mathematics/Finance/Financial Engineering

Tools

CI/CD tooling

Python libraries

Job description

Build the future with us

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.

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 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.

What could accelerate your success in this role

We’re looking for someone who:

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.

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

Education & Experience

• 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.

Nice-to-Have Qualifications

• 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.

Why you’ll love working with us

A work environment where learning and development merge with a collective pursuit of excellence;

A healthy, safe, fair, and inclusive environment where potential can be freely expressed and developed;

The opportunity to work in a hybrid environment, supported by flexibility and access to inspiring and innovative workspaces;

Competitive benefits: Flexible group insurance, competitive pension plan, stock purchase plan, vacation and wellness/personal development days, telemedicine, employee and family assistance program, ergonomic furniture program, performance bonus, discounts on iA products, and much more!

The typical hiring range for this position is between 70,000$ and 110,000$ CAD per year; the base salary offered may vary depending on knowledge, skills, years of experience, and internal equity related to the role. At iA, we are committed to offering a fair, equitable, and market-based compensation structure. Our market data is updated annually to reflect the most current market conditions.