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.

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 strong understanding of financial concepts related to portfolio management.

It is not a quantitative research or portfolio management role.





Quantitative Data Product Engineering 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 explicit business definitions, ownership, data-quality expectations, and modelling standards.

Build appropriate controls for data quality, reconciliation, monitoring, recoverability, and operational support.

Balance immediate delivery needs with long-term reliability, standardization, and reuse.

Work directly with portfolio managers, performance, risk, and other investment teams.

Understand existing analytical workflows and help modernize them into scalable data products.

Communicate technical designs, limitations, and operational considerations clearly.

Support adoption and continuous improvement of delivered products.

Core Technical Requirements Strong Python development skills,



including experience building complex analytical or data-processing workflows.

Strong SQL and data-modelling skills.

Ability to use AI effectively to accelerate software and analytics development while maintaining appropriate engineering, testing, and production-quality standards.

Experience developing reliable data pipelines and products used in production.

Experience with Google Cloud Platform and BigQuery, or comparable cloud data platforms.

Experience using Power BI, Streamlit, or comparable tools.

Strong understanding of testing, documentation, deployment, monitoring, and production support.

Experience with Git-based development, code reviews, automated deployment, and modern software-engineering practices.

Experience working with investment, financial, risk, or other complex analytical data.

Strong understanding of quantitative concepts and time-series data.

Familiarity with investment domains such as financial reference and market data, including issuers, counterparties, cash and derivative financial instruments; market-data providers such as Bloomberg, MSCI, FTSE, and Refinitiv; and positions, performance, attribution, exposures, or risk.

Ability to translate financial calculations and investment workflows into scalable technical solutions.

Product-oriented approach focused on long-term usability and supportability.

Clear communication with technical and non-technical stakeholders.

Focus on quality, governance, documentation, and continuous improvement.

Undergraduate or master’s degree in Computer Science, Engineering, Mathematics, Finance, Financial Engineering, or a related discipline.

Eight or more years of relevant experience in quantitative engineering, data engineering, analytics engineering, or financial data solutions.

Demonstrated experience delivering and supporting production-grade data or analytical products.

Experience working at the intersection of software engineering, quantitative analytics, and financial data.

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