Heading

Toronto, ON

Internship

Onsite

$65K/yr - $140K/yr

Intern

Bank of Montreal (BMO) is hiring a Quantitative Analyst co-op/intern to apply advanced analytics and quantitative methods to large datasets. The role develops predictive and machine-learning solutions, identifies trends, and provides data-driven recommendations to support business decisions and planning.

Apply Now

Responsibilities

Uses advanced analytical algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence) to mine and analyze large sets of structured and unstructured data to obtain insights. Designs and constructs new processes for modeling data. Develops predictive models and leverages big data technology to design solutions that deliver smarter business decisions, improve customer experience, and drive productivity. Collaborates with other data and analytics professionals and teams to optimize, refine and scale analysis into mature analytics solutions
Plays an active role in the futuristic display of data, and advancement of innovative data strategies to understand consumer trends and address business problems
Uses data mining and extracting usable data from valuable data sources to assess feasibility of AI/ML solutions for improved processing and usage of organization data
Conducts large-scale analysis of information to discover patterns and trends by combining different modules and algorithms
Uses analysis to provide recommendations and advice for business leaders to maintain to maintain market competitiveness
Develops prediction systems and machine learning algorithms. Investigates additional technologies and tools for developing innovative data solutions for business stakeholders
Collaborate together with the product team and partners to understand and provide data-driven decision making, business planning and future roadmap
Focus is primarily on business/group within BMO; may have broader, enterprise-wide focus
Exercises judgment to identify, diagnose, and solve problems within given rules
Works independently on a range of complex tasks, which may include unique situations
Broader work or accountabilities may be assigned as needed
Take measured risks while protecting the bank by applying our Risk Management Framework in the execution of your role, in line with our Risk Culture and within our approved Risk Appetite, making sound and risk informed decisions that align to business strategy, protect assets, and adhere to applicable policy documents (Frameworks, Policies, Standards, Procedures and Supporting documents), laws and regulations

Qualification

Required

Currently pursuing or recently completed a Master's degree or PhD in Quantitative Finance, Financial Engineering, Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative discipline
Strong academic foundation in quantitative analysis, statistical modeling, and computational methods
Proficiency in Python or other programming languages used for quantitative analysis and data science
Experience developing and leveraging APIs to extract, integrate, and automate data workflows
Knowledge of quantitative modeling, process automation, and machine learning techniques
Experience creating data visualizations, reports, and interactive dashboards to support decision-making
Understanding of Bayesian statistics and their application to real-world analytical problems
Knowledge of financial engineering, quantitative risk analysis, or related financial modeling concepts
Ability to work with large and complex datasets to generate meaningful insights and recommendations
Strong analytical, problem-solving, and communication skills
Deep learning
Machine learning
Trust, bias and ethics
Creative thinking
Critical thinking
Mathematics, statistics & operations research
Big data
Data visualization
Computational thinking and programming
Data wrangling
Data preprocessing
Complex problem solving
Analytical acumen
Creative reasoning
Verbal & written communication skills
Collaboration & team skills
Analytical and problem solving skills
Influence skills
Data driven decision making
Typically between 1-3 years of relevant experience and post-secondary degree in related field of study or an equivalent combination of education and experience
Technical proficiency gained through education and/or business experience

Preferred

Experience applying machine learning or statistical techniques within a financial services environment
Exposure to cloud-based analytics platforms, data engineering tools, or advanced quantitative research methods
Demonstrated interest in quantitative finance, financial markets, and data-driven decision making