BM Quantitative Developer

Application Deadline: 10/16/2026

Address: 100 King Street West

Job Family Group: Data Analytics & Reporting

This Role Is for You If...

If the opportunity to tackle some of the hardest challenges in finance excites you, this role is for you. You will help build the data pipelines and research technology behind stock forecasting, turn quantitative ideas into real investment capabilities, and contribute to the systems used to deploy billions of dollars in investment capital. This is an opportunity to apply your technical skills to consequential, real-world problems while learning alongside experienced quantitative researchers, developers, and investment professionals.

Program Overview

We are seeking an undergraduate student who has completed their third year of study to join the Alpha Research Team for a 12-month co‑op placement. The co‑op student will help build the technology, data infrastructure, and research tools that support security forecasting, quantitative research, portfolio analytics, and investment decision‑making. Working closely with quantitative researchers, analysts, portfolio managers, and data engineering teams, the successful candidate will gain sustained, hands‑on experience developing practical solutions used across investment workflows.

Key Responsibilities

• Research Platform Development: Help build and enhance tools, libraries, and frameworks for security forecasting, quantitative research, factor modeling, portfolio analysis, machine learning workflows, and portfolio analytics.
• Data Engineering & Integration: Support pipelines that ingest, validate, transform, and distribute financial datasets; contribute to data‑quality, integrity, and reliability checks.
• Application & Tool Development: Develop dashboards, APIs, utilities, and analytical applications that improve research and portfolio‑management workflows.
• Research Implementation: Work with researchers and analysts to translate models, forecasting signals, and analytics into reliable, reusable, and well‑documented code.
• Cloud & Infrastructure: Assist with deployment, automation, monitoring, and operational support for quantitative solutions in cloud or on‑premises environments.
• Software Engineering Practices: Participate in code reviews, testing, documentation, version control, and CI/CD workflows to improve reliability and reproducibility.
• Cross‑Team Collaboration: Gather requirements and deliver practical technology solutions in partnership with investment and technology professionals.
• Continuous Improvement: Explore new development frameworks, data tools, and quantitative technologies that may improve team capabilities and efficiency.

Candidate Profile

• Education: Currently enrolled in an undergraduate co‑op program in computer science, software engineering, mathematics, financial engineering, data science, statistics, or a related field, with third‑year studies completed before the placement begins.
• Programming: Working proficiency in Python and SQL, supported by coursework, internships, co‑op experience, research, or personal projects.
• Data Skills: Experience working with structured or unstructured datasets and an understanding of data validation, transformation, and analysis.
• Development Foundation: Familiarity with object‑oriented programming, modular design, testing, version control, and maintainable code.
• Problem Solving: Strong analytical skills, attention to detail, curiosity, and willingness to work through open‑ended technical problems.
• Collaboration: Ability to communicate clearly, receive feedback, document work, and contribute effectively in a team environment.
• Investment Interest: Interest in quantitative finance, investment management, financial markets, or the application of technology to investment research.

Preferred Skills

• Development Workflows: Experience with Git, pull requests, code review, automated testing, or CI/CD concepts.
• Data & Compute: Exposure to Pandas, Polars, DuckDB, Spark, or similar data‑processing frameworks.
• Cloud & APIs: Familiarity with AWS, Azure, or GCP, as well as APIs, microservices, or data‑integration frameworks.
• Workflow Tools: Exposure to Airflow, Prefect, Docker, or similar orchestration and containerization technologies.
• Machine Learning: Coursework or project experience involving machine learning, experiment tracking, or model deployment concepts.
• Financial Technology: Experience with financial datasets, portfolio analytics, risk models, or investment systems is an asset, but not required.

What the Co‑op Student Will Gain