Data Science Team Overview

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, the analytics team focuses on delivering trusted analytical data products, scalable analytics solutions, and standardized investment datasets that enable consistent decision-making across investment teams. The team helps modernize the data and analytics foundation that powers reporting, quantitative analysis, portfolio insights, and AI-enabled investment workflows.

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, the analytics team focuses on delivering trusted analytical data products, scalable analytics solutions, and standardized investment datasets that enable consistent decision-making across investment teams. The team helps modernize the data and analytics foundation that powers reporting, quantitative analysis, portfolio insights, and AI-enabled investment workflows.

Role Overview

The Associate Director designs and supports data and analytics products,



as well as visualization solutions, for investment teams. The Associate Director is responsible for the full product lifecycle, including design, development, testing, deployment, documentation, monitoring, and production support. The role combines strong development expertise, quantitative and financial knowledge, and an excellent command of modern data engineering practices. The Associate Director addresses the complex needs of investment teams by implementing well-designed, governed, reusable, and scalable data solutions. The Associate Director also leverages AI powered development and analytics capabilities to accelerate the delivery of high-value solutions, modernize existing solutions, reduce technical debt, and develop new analytical products. In addition, the Associate Director demonstrates strong leadership skills and can guide and mobilize teams, both functionally and across organizational boundaries, on the projects for which the Associate Director is responsible. This is a data engineering-focused role that requires a robust understanding of financial concepts related to portfolio management. It is not a quantitative research or portfolio management role.

What You’ll Accomplish With Us

Quantitative Data Product Engineering

Design and develop governed, documented,



and reusable quantitative data products that provide consistent investment data and calculations across the organization.

- Develop complex analytical workflows in Python, including financial calculations, time-series processing, performance, risk, and fund analytics.
- Transform analytical logic into tested, documented, and production-quality solutions.
- Use AI-assisted development to accelerate the design, modernization, testing, and delivery of analytical products.
- Develop visualizations, and analytical applications using tools such as Power BI and Streamlit.
- Translate investment requirements and business concepts into clear, maintainable technical designs.

Data Modelling and Cloud Engineering

- Design trusted and reusable investment data models that address the business needs of data consumers.
- Use libraries such as dbt for transformations, testing, documentation, lineage, and governed data modelling.
- Develop scalable pipelines and curated datasets across investment-data domains, and orchestrate them effectively using tools such as Prefect or Airflow.
- Apply clear business definitions, ownership, data-quality expectations, and modelling standards.
- Contribute reusable engineering patterns and shared platform capabilities.

Production Ownership and Reliability

- Build appropriate controls for data quality, reconciliation, monitoring, recoverability, and operational support.
- Investigate production issues, identify root causes, and implement sustainable solutions.
- Ensure products are observable, well documented, and maintainable by other team members.
- Balance immediate delivery needs with long-term reliability, standardization, and reuse.

Investment Partnership

📌 Senior Analyst, Quantitative Data Science (Quebec City)
🏢 iA Financial Group (Industrial Alliance)
📍 Quebec City