Quantitative Investment & Risk Analyst

CI Financial•Toronto, ON

56d•Onsite

About The Position

At CI, we see a great place to work as one that is a safe place for everyone to have a voice, where people are empowered to take ownership over meaningful work, where there is an opportunity to grow through stretching themselves, where they can work on innovative products and projects, and where employees are supported and engaged in doing so. CI GAM is advancing its investment analytics capabilities to better support Portfolio Managers and product/investment stakeholders through more technology-enabled, insight-driven analysis. This role sits at the intersection of investment analytics, quantitative/risk methodologies, and data/technology execution. The successful candidate will play a key role in transforming complex fund and portfolio datasets into clear, decision‑useful insights. Position Summary This Data Analyst position will support the Investment and Product team at Company by delivering data-driven insights that inform strategic investment decisions and product development. Reporting to the Director, Business Analytics & Insights, this individual will be responsible for gathering, analyzing, and interpreting complex investment and product-related data to support initiatives that enhance portfolio performance, product positioning, and market competitiveness. This role is ideal for a detail-oriented, analytical professional who is passionate about leveraging data to optimize investment outcomes and guide high-impact business strategies. While broad expertise is not required, candidates must demonstrate strong proficiency in SQL and data fundamentals, along with credible depth in investment analytics or quantitative/risk foundations (ideally both). This is a hands‑on role involving large-scale data, analytical production, and close partnership with both investment and technology teams.

Requirements

Bachelor’s degree in a relevant discipline (e.g., Finance, Economics, Mathematics, Statistics, Data Science, Data Engineering) or equivalent practical experience.
3–5 years of relevant experience in investment analytics, portfolio analytics, performance/risk analytics, or data-driven investment support—preferably within an asset manager or asset owner environment.
Advanced SQL proficiency with the ability to independently manage large relational datasets and complex joins.
Familiarity with market/investment data platforms.
Strong quantitative reasoning and comfort working with imperfect or incomplete data while maintaining analytical rigor.
Excellent attention to detail, strong business logic, and a high standard for accuracy and consistency.
Strong written and verbal communication skills; effective when working across investment, product, and technology teams.

Nice To Haves

Experience with Snowflake or other modern data platforms; knowledge of data warehousing concepts.
Familiarity with factor-based risk platforms.
Experience with Tableau or similar visualization tools.
Programming skills (eg, Python) for analytics, automation, or prototyping.
Exposure to data architecture, data modeling, metadata, lineage, or data engineering workflows.
Progress toward or completion of CFA or FRM.

Responsibilities

Deliver Decision‑Useful Insights Partner with Portfolio Managers and investment stakeholders to translate analytical questions into structured analyses that directly inform investment decisions.
Investigate performance and risk outcomes; identify key drivers, notable shifts, anomalies, and emerging themes across funds and portfolios.
Own the Data-to-Insight Workflow Use advanced SQL to extract, join, transform, and validate large datasets across holdings, performance, risk, and reference/security data.
Build analysis-ready datasets and reusable outputs that enhance consistency, efficiency, and scalability.
Performance & Risk Reporting & Dashboarding Develop and maintain recurring performance, risk, product, and portfolio analytics reports for investment leadership.
Support competitive and peer analysis to inform portfolio construction and product positioning.
Enable Automation and Scalable Analytics Contribute to automation initiatives and tooling that enhance the efficiency and sustainability of analytics and reporting workflows.
Support the evolution toward more technology-enabled, self‑serve analytics for investment and product teams.
Strengthen Data Quality and Platform Foundations Collaborate with data engineering and technology partners to define business logic, document requirements, validate implementations, and drive consistency in business rules and definitions.
Contribute to data quality checks, controls, and metadata/lineage improvements across analytics processes.
Conduct Targeted and Strategic Analysis Execute ad hoc deep‑dive analyses to support strategic initiatives, or address questions from senior investment and product leadership.
Communicate findings clearly, with emphasis on assumptions, logic, and decision relevance.
Communicate and Document with Precision Produce clear, structured documentation that is testable and implementable by data engineering teams.
Present analytical outcomes in concise, stakeholder‑friendly language.