Structured Solutions and Nonlinear ETFs Intern, Winter 2027

As a co-op/intern student at BMO, you will have the opportunity to be heard, keep growing and make a difference. You will be part of our campus program to gain the skills and knowledge needed to take on roles similar to the description listed below.

Our student experience is designed to integrate you to the BMO team from day one by adding value in the work you do. You will have the opportunity to participate in programs such as the Women in Banking Mentorship Program, BMO Social Squad student‐led activities, BMO Academy learning platform and access to various Employee Resource Groups to further develop your network within BMO.

Interested in learning more about our campus program? Stay up‐to‐date with BMO Campus Recruitment by following us on Instagram @bmocanada | @bmo_us and joining our LinkedIn group BMO Campus Recruiting & Early Talent.

About The Team
The Structured Solutions & Non‐Linear ETFs team develops and manages innovative investment strategies that utilize derivatives, structured products, quantitative models, and ETFs. The team is responsible for designing, pricing, executing, and monitoring portfolios that incorporate options, structured notes, volatility strategies, and non‐linear payoff profiles. The group works at the intersection of portfolio management, quantitative research, derivatives structuring, and technology, using advanced analytical tools to develop investment solutions for clients.

Position Overview
We are seeking a highly quantitative and technically skilled intern to join our Structured Solutions & Non‐Linear ETFs team. This role is ideal for students with strong backgrounds in mathematics, engineering, computer science, financial engineering, statistics, or quantitative finance who are interested in applying analytical and programming skills to real‐world investment problems.

The successful candidate will work directly with Portfolio Managers and Quantitative Researchers on projects involving derivatives pricing, Monte Carlo simulations, portfolio analytics, risk management, ETF research, and investment technology development.

This is a hands‐on role where interns will contribute to production‐level analysis and tools used in the management of multi‐billion‐dollar investment portfolios.

Key Responsibilities
Quantitative Research & Model Development

Develop and enhance quantitative models used in portfolio construction and security selection.

Perform Monte Carlo simulations for structured notes, options, and path‐dependent investment strategies.

Analyze return distributions, downside risk, volatility dynamics, and probability‐weighted investment outcomes.

Research factor‐based investment strategies and systematic portfolio construction techniques.

Conduct backtesting and historical scenario analysis for investment strategies.

Derivatives & Structured Products Analytics

Assist in pricing and valuation of structured products and derivative portfolios.

Analyze option sensitivities including delta, gamma, vega, theta, and rho.

Evaluate risk‐return trade‐offs of autocallables, buffered products, covered call strategies, and other non‐linear investment structures.

Build tools to monitor portfolio greeks, profit‐and‐loss attribution, and risk exposures.

Support the modelling of structured note cash flows and payoff mechanics.

Software Development & Automation

Develop analytical tools using Python, SQL, and VBA.

Build and maintain databases used for portfolio management and investment analytics.

Automate portfolio reporting, risk monitoring, and investment workflows.

Design data pipelines that integrate market data, portfolio holdings, and risk analytics.

Improve the efficiency and scalability of existing investment processes.

Portfolio Management Support

Monitor portfolio exposures and performance drivers.

Analyze market events and assess potential impacts on derivative positions.

Assist with investment research on equities, ETFs, volatility markets, and structured products.

Prepare analyses for portfolio reviews and investment committee discussions.

Qualifications
Required

Pursuing a degree in:

Engineering

Applied Mathematics

Statistics

Computer Science

Physics

Financial Engineering

Quantitative Finance

Mathematical Finance

Or a related quantitative discipline

Strong programming skills in Python.

Strong understanding of probability, statistics, and numerical methods.

Advanced Excel skills.

Excellent analytical and problem‐solving abilities.

Ability to work with large datasets and complex financial models.

Preferred

Experience with Monte Carlo simulation methodologies.

Knowledge of derivatives pricing and option theory.

Experience with SQL and database development.

VBA pr