Job descriptionSenior Quantitative Researcher – Alpha Research Team Location: Toronto, ON
Company: BMO Global Asset Management
Team: Alpha Research Team
Role Overview We are seeking a Senior Quantitative Researcher to join our team and contribute to a wide range of quantitative initiatives. This role supports portfolio management and research through alpha modeling, risk modeling, and optimization techniques, collaborating with multiple investment teams to conduct research, develop models, and work with deployment teams for production implementation.
Responsibilities
Conduct research on alpha signals across diverse datasets and investment universes; analyze factor behavior across different market environments and asset classes.
Code, train, and deploy statistical and machine‑learning models to deliver repeatable and actionable insights.
Apply portfolio optimization techniques to achieve greater risk‑adjusted returns.
Monitor exposures, limits, and risk metrics across portfolios; develop and maintain risk models to support investment decisions.
Work with large datasets to extract signals, clean data, and ensure data integrity; collaborate with data engineering teams to improve data pipelines and infrastructure.
Partner with multiple investment teams to provide quantitative insights and solutions; communicate complex concepts clearly to non‑quant stakeholders.
Explore new models, techniques, and technologies to enhance investment processes; stay current with industry trends and academic research in quantitative finance.
Assist with ad‑hoc projects across asset classes, including equities, multi‑asset, and derivatives; contribute to risk analysis, factor modeling, and performance studies for various mandates.
Qualifications
4–6 years in a similar research role.
Strong foundation in quantitative finance, alpha research, risk modeling, and portfolio analytics.
Experience deploying quantitative models into live portfolio processes under operational, performance, and risk constraints.
Proficiency in programming languages such as Python and SQL.
Proficiency in machine‑learning algorithms, concepts, and deployments.
Experience with financial datasets including fundamentals, estimates, sentiment, macro, factor risk models, transaction‑cost models, and security masters.
Excellent problem‑solving skills and the ability to work collaboratively.
Preferred Skills
Strong development hygiene for research code: testing frameworks, modular design, reproducibility, and maintainable codebases.
Experience with ML lifecycle tooling (experiment tracking, model versioning/monitoring).
Experience with data orchestration/scheduling tools (e.g., Airflow, Prefect) and building reliable ETL/ELT workflows.
Strong discipline around data quality checks, point‑in‑time handling, and traceability (data lineage/auditability) for research.
Strong communication and interpersonal skills.
Education
Graduate degree in Financial Mathematics, Engineering or a related field (preferred).
CFA designation (preferred).
BMO is committed to an inclusive, equitable and accessible workplace. We provide accommodations on request for candidates participating in all aspects of the selection process. To request an accommodation, please contact your recruiter.
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