Manager, Credit Decisioning Systems

Questrade Financial Group•Toronto, ON

1d•CA$105,000 - CA$130,000•Hybrid

About The Position

Questrade Financial Group (QFG) is a diverse financial services organization that leverages cutting-edge technology to enhance the financial success and security of Canadians. The company combines human-centric collaboration with AI-driven innovation to redefine financial services. This role is for a Manager, Credit Decisioning Systems at Flexiti, a part of QFG, responsible for the end-to-end administration, configuration, and performance of Flexiti’s “Buy Now, Pay Later” automated lending platforms. The role involves leading the technical implementation of credit policies, managing data ingestion, and overseeing the deployment and monitoring of Machine Learning (ML) models for real-time lending decisions. This position supports the Credit Risk Strategy team in ensuring the effective implementation and continuous monitoring of risk systems, with a focus on data integrity and system performance.

Requirements

Advanced proficiency in at least one major platform (Provenir, FICO Blaze/Platform, TakTile, Experian PowerCurve, or Zest AI).
Strong ability to analyze data, identify patterns, and effectively debug system issues.
Proactive and agile mindset with a keen ability to identify and contribute to the resolution of technical and process challenges.
Excellent interpersonal skills and a team-oriented approach, capable of working effectively with cross-functional teams and internal/external partners.
Meticulous in monitoring system performance and ensuring the accuracy of data and strategy implementations.
Proficiency in SQL, Python or R, for rule creation, complex data extraction, model integration and data analysis.
Familiarity with JSON/XML for API integration, DataBricks and BigQuery.
Organized, ability to work independently and can handle pressure of tight timelines or emergencies.
University degree in a quantitative field, Economics, Business, Statistics, Mathematics, Engineering or equivalent (Master’s is an asset)
3-5 years of total experience in credit risk, fintech, or banking operations.
3+ years of direct experience managing automated decisioning engines.
2+ years in a leadership or senior-level role overseeing technical projects or small teams.
Experience managing an Adjudication decisioning platform is required.

Nice To Haves

Basic understanding of credit risk concepts, lending products, or fraud-related systems is a plus.

Responsibilities

Assist in the design, coding, and testing of credit strategies for various lending products and the full credit lifecycle of customers.
Support the integration of credit systems with core banking platforms and collaborate with vendors to implement optimal solutions.
Utilize SQL, Python, BigQuery, and Data Bricks extensively to pull data, monitor strategy migrations, and conduct analytics to debug issues and validate updates within the risk systems.
Monitor and leverage real-time reporting mechanisms to ensure correct strategy execution and identify potential issues promptly.
Assist in implementing, updating, and maintaining credit models and strategies collaborating with Credit Risk Strategy teams to "productionize" Python/R-based ML models within the decisioning workflow, ensuring proper mapping of variables.
Participate in the process for user testing changes in strategies and models, ensuring accuracy and functionality.
Identify and suggest enhancements and opportunities for process optimization and cost reduction within risk systems.
Manage the daily operations of the credit decisioning engine, ensuring 99.9% uptime for real-time application processing.
Design and maintain SQL-based ETL processes to load internal and external data (Bureau, KYC, Alt-Data) into the decisioning environment as required.
Establish dashboards to monitor decisioning latency, and "approval/decline" distributions. Ensure all system changes comply with OSFI (Canada).
Serve as the primary technical point of contact for platform vendors, managing upgrades, API integrations, and troubleshooting.
Lead User Acceptance Testing (UAT) for all logic changes and manage "Champion/Challenger" A/B testing strategies to optimize portfolio performance.