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Location: Toronto, ON

Company: BMO Global Asset Management

Team: Alpha Research Team

We are seeking a Senior Quantitative Researcher to join our team and contribute to a wide range of quantitative initiatives. This role involves supporting portfolio management and research activities through alpha modeling, risk modeling, and optimization techniques. The successful candidate will collaborate with multiple investment teams to conduct investment research, develop models and work with deployment teams for production implementation.

• Alpha Research: Conduct research on alpha signals across a wide variety of datasets and investment universes; Analyze factor behavior across different market environments and asset classes
• Alpha Modeling: Code, train and deploy statistical and machine learning models to deliver repeatable and actionable insights
• Portfolio Optimization & Factor Analysis: Apply portfolio optimization techniques to achieve greater risk adjusted returns
• Portfolio Analysis & Risk Management: Monitor exposures, limits, and risk metrics across portfolios; Develop and maintain risk models to support investment decisions
• Data Management & Analytics: Work with large datasets to extract signals, clean data, and ensure data integrity; Collaborate with data engineering teams to improve data pipelines and infrastructure
• Cross-Team Collaboration: Partner with multiple investment teams to provide quantitative insights and solutions; Communicate complex concepts clearly to non-quant stakeholders
• Research & Innovation: Explore new models, techniques, and technologies to enhance investment processes; Stay current with industry trends and academic research in quantitative finance
• Broader Quantitative Support: 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

• 4-6 years in a similar research role
• Strong foundation in quantitative finance, alpha research, risk modeling, and portfolio analytics
• Experience working in environments where quantitative models are deployed into live portfolio processes and subject to operational, performance, and risk constraints
• Strong 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, security masters, etc.
• Excellent problem-solving skills and ability to work in a collaborative environment

• Strong development hygiene for research code: testing frameworks, modular design, reproducibility, and maintainable codebases
• Experience with ML lifecycle tooling (e.g., experiment tracking, model versioning / monitoring)
• Experience with data orchestration / scheduling tools (e.g., Airflow, Prefect or similar) 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

• Graduate degree in Financial Mathematics, Engineering or related field preferred
• CFA preferred

The above represents BMO Financial Group’s pay range and type.

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.

BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit: