Senior Analyst, Risk Data Scientist

TMX Group•Montreal, QC

2d•Hybrid

About The Position

The TMX Post Trade Risk Management team manages financial risks for The Canadian Depository for Securities Limited (CDS) and The Canadian Derivatives Clearing Corporation (CDCC), developing and maintaining robust procedures, models, systems, and reporting aligned with TMX policies, TMX risk appetite, and PFMIs. Reporting to the Senior Manager, Risk Integration, this role sits at the intersection of quantitative finance and software engineering. You will be a driving force behind our Core Risk Libraries and Calculation Applications. Your mandate is to design the foundational financial mathematics, package them into shared Python libraries, and build the robust calculation applications that process raw transactional data into actionable risk metrics. Collaborating closely with our Infrastructure and Reporting pillars, you will ensure our mathematical models are scalable, accurate, and seamlessly integrated into production.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a Master’s degree in Financial Engineering (or a related field with a strong focus on finance and data science).
3–5 years of hybrid experience bridging finance and programming; proven application of data science techniques in financial risk management.
Advanced proficiency in Python (including OOP, Pandas, NumPy, and scientific computing libraries).
Strong knowledge of SQL and database querying.
Experience with code versioning (Git) and software development lifecycles.
Strong time management, analytical skills, and the ability to prioritize complex technical projects in a high-pressure environment.

Nice To Haves

FRM, PRM, or progression within the CFA program.
Familiarity with LaTeX for mathematical documentation, Atlassian product suite (Jira, Confluence), and basic knowledge of containerization (Docker) or artifact management (Nexus).
Familiarity with Business Intelligence tools (e.g., Tableau).

Responsibilities

Design, develop, and maintain the foundational Python libraries containing core financial logic and mathematical models.
Package and publish these libraries to our central artifact repository.
Develop the UAT tools and frameworks necessary to validate third-party integrations against strict risk logic before they go live.
Consume core libraries to architect and build robust, scalable risk calculation applications.
Ensure these applications efficiently process complex models and write high-quality analytical outputs to our databases for downstream reporting.
Apply advanced data science and quantitative techniques for the in-depth analysis of equities, fixed income, derivatives, and structured products.
Collaborate with the Analytics Infrastructure owner to containerize applications, optimize computational performance, and adhere to CI/CD best practices.
Create and maintain comprehensive application documentation, solution diagrams, and mathematical flowcharts.