Address:

• Portfolio Research Leadership: Define and lead a portfolio research agenda focused on improving investment outcomes, portfolio robustness, and the consistency of decision-making across strategies and mandates.

• Investment Insights and Advisory: Serve as a senior research partner to portfolio managers and investment leaders; identify portfolio risks, unintended exposures, concentration concerns, and opportunities to improve risk-adjusted returns.

• Portfolio Construction and Optimization: Develop and evaluate portfolio construction frameworks, optimization techniques, constraints, and sizing methodologies that balance expected return, risk, liquidity, capacity, turnover, and implementation costs.

• Optimizer Constraint Research and Calibration: Lead research on tuning portfolio optimizer constraints through robust backtesting across different investment signals, market regimes, and mandates; evaluate their impact on risk-adjusted returns, turnover, concentration, liquidity, capacity, stability, and implementation costs.

• Risk and Exposure Management: Advance the use of factor risk models, scenario analysis, stress testing, active risk, systemic risk, predicted beta, and other portfolio diagnostics to support effective oversight and challenge.

• Performance and Decision Analysis: Lead performance attribution, holdings-based diagnostics, and decision-quality studies to distinguish repeatable investment skill from factor, market, or implementation effects.

• Research Governance: Establish robust standards for research design, validation, documentation, reproducibility, model monitoring, and change management; ensure analytical outputs are transparent, auditable, and fit for use in live investment processes.

• Cross-Team Collaboration: Partner with investment, risk, data, technology, product, and distribution teams to develop practical solutions, improve shared capabilities, and communicate portfolio insights to both technical and non-technical stakeholders.

• People Leadership and Mentorship: Lead, coach, and develop quantitative researchers and analysts; set clear priorities, promote constructive challenge, and foster a culture of intellectual curiosity, accountability, and continuous learning.

• Innovation and Strategic Development: Evaluate emerging datasets, quantitative methods, machine learning techniques, and research technologies; prioritize innovations that provide measurable investment or operational value.

• 10+ years of experience in portfolio research, quantitative investing, portfolio management, risk management, or a closely related investment role.

• Demonstrated experience leading complex research programs and influencing portfolio decisions across multiple investment teams or mandates.

• Deep expertise in portfolio construction, optimization, factor analysis, risk modeling, performance attribution, and empirical investment research.

• Strong understanding of how quantitative models operate within live investment processes, including implementation, liquidity, capacity, transaction cost, operational, and governance constraints.

• Advanced proficiency in Python and SQL, with the ability to review analytical methodologies, challenge assumptions, and guide scalable implementation.

• Experience working with financial datasets such as fundamentals, estimates, sentiment, macroeconomic data, factor risk models, security masters, holdings, transactions, and performance data.

• Proven ability to communicate complex research clearly and persuasively to portfolio managers, senior executives, clients, and non-quantitative stakeholders.

• Strong leadership, judgment, and prioritization skills, with a track record of developing talent and delivering high-quality outcomes in a collaborative environment.

• Experience supporting a broad range of investment mandates, including mutual funds, ETFs, model portfolios, institutional portfolios, or multi-asset strategies.

• Experience with commercial risk models and investment platforms such as Barra, Axioma, Bloomberg, FactSet, or similar tools.

• Knowledge of machine learning, alternative data, model validation, experiment tracking, and model monitoring within an investment context.

• Experience modernizing research workflows, reducing technical debt, and moving analytical processes from ad hoc research into reliable production environments.

• Familiarity with cloud platforms, workflow orchestration, data lineage, CI/CD practices, and scalable research infrastructure.

• Ability to connect detailed quantitative analysis with broader investment, product, client, and business considerations.

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: