Data Analyst; to enterprise stress testing and credit risk forecasting using Pytho

Data Analyst (3+ years) to support enterprise stress testing and credit risk forecasting using Python-based data workflows- 41570  Our financial services client is seeking a  Data Analyst (3+ years) to support enterprise stress testing and credit risk forecasting using Python-based data workflows- 41570
Location Address:
Hybrid - Toronto – 3 days/week (no fixed days – flexible)
Contract Duration: 6 months (Possibility of extension & conversion to FTE)
Schedule

Hours:

8:30am-5pm Monday-Friday; standard 37.5 hrs/week
Story Behind the Need
Business Group & Project:
Enterprise Stress Testing's (EST) mandate is to design, develop, and execute the Bank's enterprise-wide stress testing program. The team is responsible for assessing the potential impact of adverse economic and business scenarios on the Bank's credit portfolio and overall financial performance.
The Credit Risk Stress Testing team is seeking a Contract Data Analyst to support the modernization, optimization, and implementation of Python-based stress testing processes. The successful candidate will contribute to credit risk forecasting and scenario analysis initiatives, helping the Bank assess potential credit losses under various economic conditions.
This role will specifically support the FSAM2 (Financial Sponsor / Asset Management) project, a regulatory-driven initiative focused on enhancing the Bank's approach to stress testing and assessing potential credit losses associated with the Financial Sponsor and Asset Management portfolio. The project is currently in its implementation phase, with methodology development nearing completion and technical execution underway.
The Data Analyst will focus on improving the performance of data-intensive Python workflows, refactoring code for scalability and maintainability, and developing new data ingestion and consolidation processes from multiple upstream data sources. The role will also support future phases of the project involving the implementation and integration of newly developed credit risk models.
Team Structure: 8 FTEs organized by portfolio coverage (e.g., Canadian Retail Banking, Business Banking).
Project Status:  Phase 2 is currently underway and focused on implementing methodologies into production processes. Phase 2 is expected to continue through April 2027 (the current contract end date), with Phase 3 anticipated to involve broader cross‑functional collaboration and implementation of additional credit risk models. There is potential for extension beyond the initial contract term.
Typical Day in Role:
Design and develop data solutions to integrate new data sources into existing stress testing platforms and support new stress testing initiatives.
Develop and enhance Python-based processes to source, transform, validate, and consolidate data from multiple upstream systems.
Analyze existing codebases and implement changes based on evolving stress testing methodologies and business requirements.
Optimize the performance of large-scale, data-intensive Python notebooks and workflows.
Refactor code to improve readability, maintainability, scalability, and efficiency while preserving business logic.
Ensure outputs are accurate, reliable, and well-documented for downstream analytics, reporting, and modeling purposes.
Collaborate with internal stakeholders, model developers, and risk partners to gather requirements and validate results.
Create technical documentation and provide knowledge transfer to support long-term sustainability of solutions.
Support end-user data processing and calculations through Python-based scripting and automation.
Gain exposure to IFRS 9 methodologies, stress testing frameworks, and credit risk forecasting processes.
Candidate Value Proposition:
The successful candidate…