Senior Quantitative Analyst

Pay Details

Pay Range: 76, CAD

Toronto, Ontario, Canada

37.5 hours per week

Analytics, Insights, & Artificial Intelligence

The Model Validation (MV) group is part of the bank’s model risk management function, with a focus on the Retail model validation team that vets and approves complex mathematical and statistical models used to measure risk exposure in retail and trading products. By ensuring an objective and independent evaluation of models, the function is critical to the effective measurement and management of risk across the TD Bank Group.

• Perform validation of all models deemed in-scope by the bank-wide Model Risk Policy. These models are used for projecting RWA capital (PD/LGD/EAD), PPNR estimates for stress testing (DFAST, EWST), adjudication/management/collection for various retail products, marketing and analytics, etc.
• Develop independent benchmarks for the validation of the above listed models, using supervised, unsupervised, or deep learning algorithms. Assess the appropriateness of the model for its specific use, the reasonableness of its assumptions, and the accuracy of its implementation.
• Prepare detailed reports describing the mathematical analytics, validation techniques employed, test results obtained, and any model limitations noted.
• Prepare Management summaries highlighting the outcome of the validation process for each model and outlining recommendations for approval or further improvements.
• Establish and maintain productive working relations with internal model development groups such as US and CAD Retail Model Development, Financial Stress Testing (FST), and with external vendors who have developed customized models for TD.
• Play a key role in ensuring the appropriate use of risk models. Identify the need to implement new models or techniques as industry standards evolve and regulatory requirements change.
• Stay current in knowledge of credit risk management methodologies, predictive modeling, and statistical analysis.

• Strong statistical background and excellent analytical and problem‑solving skills with a graduate degree in one of the following areas: statistics, economics, finance, mathematics, computer science, and engineering.
• 3‑5 years of experience with model development/validation, including machine learning models, and dealing with PPNR stress testing, scorecard, and/or capital models.
• Hands‑on experience with programming languages such as Python, SAS, and R.
• Knowledge of retail banking products, customer behaviours, and macroeconomic impacts is a definite asset.
• Excellent verbal and written communication skills.
• Good time‑management and multitasking skills.
• Quick learner who grasps new concepts and techniques quickly.
• Must be a good team player.

• Mid‑Senior level

• Full‑time

• Research, Analyst, and Information Technology
• Industries:
  Investment Banking