Model Risk Analyst
Purpose
The Model Risk Analyst plays a key role in assessing model risk through model validations, risk reviews, and ongoing analysis. As part of an Enterprise Risk Management function, this individual works under senior model risk leadership to validate financial models across the organization, evaluating model reasonableness, identifying weaknesses, and assessing associated risks. The role involves interaction with model owners and users across the organization to understand model performance, development activities, and emerging risks.
Responsibilities
Perform model validations across a range of financial models, including credit risk, derivative valuation, mortgage prepayment/default, and asset-liability management models.
Develop validation reports summarizing methodologies, analyses, findings, and conclusions.
Review model changes, assess materiality, and conduct targeted validations.
Support the development of benchmarking tools and analytical frameworks, including advanced analytics or machine learning techniques for validation and performance monitoring.
Provide independent assessments on modeling practices and validation-related matters.
Contribute to third-party model validation activities.
Support internal audits and regulatory examinations, including remediation efforts.
Requirements
Advanced degree in a quantitative field such as Computational/Quantitative Finance, Statistics, Mathematics, Computer Science, Economics, or a related discipline; Ph.D. preferred.
At least one year of experience in a related field in model risk management, predictive modeling, financial modeling, optimization, or data science.
Knowledge of mortgages and mortgage-backed securities, interest rate derivatives, fixed-income analytics, prepayment modeling, interest rate modeling, probability of default, credit loss modeling, and stress testing.
Experience with stochastic processes, time series analysis, principal component analysis, optimization methods, logistic regression, and Monte Carlo simulations.
Proficiency in Python.
FAQs
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