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Lead the design and development of scalable, production-quality research and portfolio-construction capabilities using Python and related technologies, ensuring research outputs can be reliably deployed and utilized in live investment processes

We are seeking a highly experienced professional to lead high-impact portfolio research initiatives across BMO Global Asset Management. This role will shape the portfolio research agenda, develop actionable insights for investment teams, and strengthen the integration of alpha research, risk management, portfolio construction, and performance analysis into investment decision-making.

The successful candidate will operate as a senior thought partner to portfolio managers and investment leaders, combining deep quantitative expertise, strong investment judgment, and effective leadership. The role will oversee complex research programs, guide and develop scalable investment capabilities, and translate findings into practical improvements across multiple mandates and asset classes.

The role combines factor-model expertise, portfolio optimization, risk management and production-quality Python development to manage risk exposures, generate daily trade recommendations and strengthen the portfolio-construction process.

• Portfolio Research Leadership: 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.
• Mandate and Investment Objective Alignment: Develop a fundamental understanding of each portfolio’s mandate, investment philosophy, objectives, benchmark, risk tolerance, and regulatory, client, and implementation constraints; ensure portfolio construction processes are appropriately designed and calibrated to deliver the intended outcomes within those parameters.
• Portfolio Construction, Optimization, and Constraint Calibration: Develop and evaluate portfolio construction frameworks, optimization techniques, constraints, and sizing methodologies; lead robust backtesting to calibrate optimizer constraints across investment signals, market regimes, and mandates, balancing expected return against risk, liquidity, capacity, turnover, concentration, stability, and implementation costs.
• Risk Budgeting, Position Sizing, and Exposure Management: Establish risk budgets and position-sizing rules that translate alpha conviction, forecast uncertainty, liquidity, and risk capacity into transparent portfolio allocations; advance the use of factor risk models, scenario analysis, stress testing, active and systemic risk, predicted beta, and other diagnostics to identify and manage portfolio exposures effectively.
• Automated Factor Hedging & Daily Trade Recommendations: Design and implement an automated factor-hedging layer that identifies unintended factor exposures and recommends efficient risk-adjusted hedges while respecting portfolio, liquidity, turnover and transaction-cost constraints.
• Factor Model Development: Build and apply multi-factor risk models, including factor exposure estimation, factor covariance estimation, idiosyncratic return and risk estimation, and the development of custom factors and covariance methodologies.
• 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, 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.

• Factor models: factor definitions, exposure estimation, factor returns, covariance matrices, idiosyncratic returns and risk, custom factors, model diagnostics and validation.
• Portfolio optimization: objective design, return expectancy and robustness, risk and exposure constraints, turnover controls, estimation uncertainty, transaction-cost and market-impact modelling, and post-trade evaluation.
• Risk implementation: automated factor hedging, risk budgets, sizing rules, exposure monitoring, scenario analysis and daily trade-generation workflows.
• Model robustness: sensitivity to estimation windows, return frequency, weighting schemes, outlier handling, autocorrelation and alternative covariance estimators.

• Significant 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.
• Fluent 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.

• Graduate degree in Financial Mathematics, Statistics, Economics, Engineering, Computer Science, Data Science, or a related quantitative field preferred.

• CFA designation strongly preferred; other relevant investment or risk credentials are considered an asset.

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: