Quant Modeling [Multiple Positions Available]

DESCRIPTION:

Duties: Establish and maintain standards for the development of models used in Wholesale Credit loan loss forecasting and Obligor Grading and enhance standards in accordance with evolving industry practices and regulatory expectations.

Evaluate the adherence of model development processes to established standards by assessing the soundness of model design, validity of assumptions, reliability of input data, thoroughness of testing and implementation, and the appropriateness of performance metrics for Wholesale Credit loan loss forecasting and Obligor Grading models.

Perform model reviews by identifying weaknesses, limitations, and emerging risks through techniques including benchmarking, independent testing, and continuous monitoring activities.

Prepare detailed technical documentation and reports outlining model risk assessments and communicate findings and recommendations to internal stakeholders and senior management.

Support the organization in ensuring appropriate use of models on an ongoing basis and contribute to maintaining the overall model risk within the firm's risk appetite framework.

Participate in internal and external audits, as well as regulatory examinations related to model risk governance and compliance.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Financial Mathematics, Statistics, Economics, Finance or related field of study plus 4 years of experience in the job offered or as Quant Modeling, Model Risk Program, Risk Consulting or related occupation.

The employer will alternatively accept a PhD in Financial Mathematics, Statistics, Economics, Finance or related field of study plus 2 years of experience in the job offered or as Quant Modeling, Model Risk Program, Risk Consulting or related occupation.

Skills Required: This position requires experience with the following: Building bespoke credit risk models for wholesale credit portfolios including Probability of Default, Loss Given Default, and Expected Credit Loss (ECL) in using Python for Commercial & Industrial and Commercial Real Estate loans; Applying statistical and machine learning techniques including Linear & Logistic Regression, Time Series Modeling, Decision Trees, Gradient Boosting Machines, Markov Chains, and Monte Carlo Simulations; Incorporating economic factors and macroeconomic scenarios for loss forecasting, stress testing, and regulatory compliance under frameworks including Basel, Comprehensive Capital Analysis and Review, Risk- Weighted Assets, and Current Expected Credit Loss; Performing stressed loss modeling, PPNR forecasting, allowance for credit losses, discounted cash flow analysis, and portfolio-level credit loss estimation; Utilizing Python, SQL, and Excel to implement financial models, analyzing results, and generate actionable insights for risk management and regulatory reporting; conducting Linear and Logistic Regression, Time Series Modelling, Decision Trees, Gradient Boosting Machines, Markov...