Quantitative Analyst I
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TD Model Validation (MV) group is responsible for the independent validation and approval of analytical models used for risk, pricing, hedging, and capital evaluation for portfolio of financial products. This also includes validation of decision making models. Job Description
The position reports to Senior Manager, Non-Retail Model Validation group within MV. Detailed accountability include:
- Validate Non-retail Models including Credit Risk Rating and Parameter Models, General Allowance and Capital Models, Operational Risk Models, and Stress Testing Models (i.e. CCAR/DFAST/EWST), ML models and AI applications.
- Develop/implement validation methodologies and standards. Ensure that the validation methodologies and standards are in line with industry best practice or address regulatory and audit requirements and/or findings in a timely manner.
- Develop and apply a variety of statistical tests and modeling techniques to identify/recommend improvements to models and undertake related initiatives. Ensure extensive testing of models sensitivity that help assessing models behavior and risk.
- Implement and evaluate external models used for benchmarking internal model performance. Participate in model selection and related due diligence activity.
- Actively participate with business partners in internal data management to ensure data integrity and the completeness of data capture for model validation and development purpose, as well as meet Basel requirements on data capture and retention.
- Maintain full professional knowledge of techniques and developments in the field of quantitative analysis, and share knowledge with business partners and senior management.
- Strong quantitative skills with an advanced degree in one or more of the following areas: mathematics, statistics, economics, financial engineering, and computer science.
- Experience in model development or validation with an advanced knowledge of financial/econometric modeling and statistical analysis techniques; experience with econometric and credit risk capital modeling or credit rating models is an asset.
- Experience or strong knowledge of machine learning is a plus.
- Proficient in one or more programming languages such as SAS, MatLab, R, or Python.
- Excellent verbal and written communication skills.
Good time management and multitasking skills with minimal supervision. Hours
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