Credit Risk Specialist

4-5 years of Retail Credit Risk Management experience in a financial institution or similar
Experience working with credit data, either within a risk management or product context
Prior experience in automated adjudication strategy development would be considered a strong asset
Auto Finance industry experience would be considered a strong asset
High Proficiency in Python and SQL
Others such as SAS, Tableau, Angoss would be considered an asset
Prior experience in machine learning (e.g., Decision tree, XGBoost) and building models in MS Azure would be considered a strong asset
Proficiency in Big data (Hadoop, HIVE, Spark, etc.) would be considered an asset
High Proficiency in MS Office: Excel (Pivot tables and etc.), Word and PowerPoint
Flexibility and resilience to execute in an agile environment, including the ability to adapt and manage changing priorities
Analytical and intellectually curious, seeking to understand root causes
Great attention to detail
Strong ownership mentality and can do attitude
Strong presentation and communication skills
Excellent relationship management skills
Post-secondary degree is required (preferably in a quantitative field: i.e. Math, Statistics, Computer Science or similar)
A Master’s Degree is considered to be an asset

Participate in development, design, testing and verification of TDAF Automated Risk Decision for acquisition
Regularly monitor performance of existing and new ARDs, in terms of both the rate of auto-adjudication versus expectations and historical levels, and the on-book performance of loans which are funded through the various implemented ARDs
Represent Retail Risk Management as a subject matter expert in the planning of new initiatives and product launches for TDAF
Collaborate with all stakeholders including Risk Infrastructure and Reporting Team, TDBFG Risk, TDAF Product group and external vendors
Provide insights and analysis that will identify origination and delinquency trends and inform revenue enhancement and loss mitigation decisions

Prior experience in automated adjudication strategy development would be considered a strong asset
Auto Finance industry experience would be considered a strong asset
Others such as SAS, Tableau, Angoss would be considered an asset
Prior experience in machine learning (e.g., Decision tree, XGBoost) and building models in MS Azure would be considered a strong asset
Proficiency in Big data (Hadoop, HIVE, Spark, etc.) would be considered an asset
A Master’s Degree is considered to be an asset