Senior Quantitative Researcher, Global Asset Management

BMO•Toronto, ON

8d•CA$96,600 - CA$180,600•Onsite

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

Conduct research on alpha signals across a wide variety of 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