Senior Analyst, Quantitative Data Science

Job Description

Build the future with us 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, Core Analytics 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 Senior Analyst, Quantitative Data science, plays a central role in developing and scaling analytical data products used across Investments. This role combines financial domain understanding, modern data engineering, and analytics product development to transform complex investment data into trusted, reusable, and consumable assets. As a Quantitative Data Engineer, you will partner directly with investment teams to understand analytical requirements, engineer scalable solutions, and deliver end‑to‑end products that support investment decision‑making.

You will work across the full lifecycle, from data sourcing and transformation through visualization, operationalization, and continuous improvement. You will contribute to the modernization of the investment data ecosystem by developing cloud‑native data solutions, supporting advanced visualization experiences, and helping prepare analytical assets for AI‑enabled use cases. The role combines hands‑on technical delivery with product ownership, business engagement, and a strong focus on reliability and long‑term supportability.

This is a hands‑on role for someone who enjoys building high‑quality data and analytics solutions, working close to investment decision‑making, and translating financial workflows into scalable analytical products. While the role requires credible financial and quantitative literacy, it is not intended to be a Front Office quant research role.

Partner with investment teams such as Portfolio Management, Asset Allocation, Trading, Performance, Risk, Research, and other investment groups to develop and maintain analytical data products that support investment workflows. Translate financial and analytical requirements into scalable data solutions. Manage key quantitative and financial datasets, including performance, attribution, time‑series, holdings, positions, exposures, and aggregated analytics. Ensure critical investment datasets are accurate, validated, timely, and well‑governed.

Support modernization of reporting and analytical processes across Investments. Improve consistency and standardization of analytical outputs across teams. Identify opportunities to automate manual processes and improve data reliability, timeliness, and quality. Enable trusted, reusable datasets that support reporting, research, visualization, and AI initiatives.

Own the lifecycle of analytical products from data ingestion and transformation through delivery and ongoing evolution. Collaborate with stakeholders to define requirements, priorities, operating expectations, and success measures. Design scalable data models and transformation pipelines that support multiple consumers and downstream use cases. Ensure analytical products are maintainable, well‑documented, observable, and operationally supportable. Continuously improve reliability, usability, performance, and business value of analytical products.

Apply an experimentation‑driven mindset to incorporate innovation in data engineering and financial analytics delivery. Balance short‑term delivery needs with long‑term sustainability, standardization, and reuse.

Develop high‑impact analytical experiences using…