VP Model Risk Management

SECU•Raleigh, NC

4d•Hybrid

About The Position

If you are motivated and believe in the credit union philosophy of "People Helping People," join our team! Essential Responsibilities: (30%) Manage a team of model risk analysts / data scientists responsible for executing model validation activities across the model life cycle including model development, model validation, ongoing performance evaluation, and tracking model findings to ensure models across SECU are conceptually sound relative to their intended use and performing appropriately. Engage external consultants to perform validation work when necessary to include scope of required work, contract review and negotiation, and end-to-end management of vendor relationships. (20%) Provide effective challenge to model assumptions, mathematical formulations, and implementations across a range of model architectures and use cases including competing risk logistic regression, time series analysis, ordinary least squares, Monte Carlo simulation, generative large language models (Artificial Intelligence), and machine learning techniques (e.g. XGBoost). (20%) Document, rank, and effectively communicate technical modeling concerns to various audiences including model owners, senior leaders, and management committees. (15%) Serve as a technical subject matter expert on the most complex code, data processing, and financial modeling challenges across the entire organization: credit risk, ALM, liquidity, fraud, BSA/AML, capital planning and stress testing, lending and loan pricing, cybersecurity, etc. (15%) Develop and maintain effective partnerships within SECU to influence sound modeling practices, particularly with model owners, model developers, and data analysts. Represent SECU Model Risk Management in interactions with regulatory agencies and on SECU management committees.

Requirements

Masters in a quantitative discipline (Economics, statistics, finance, data science or analytics, math, physics, or related field)
10+ years of experience in modeling or analytics
Ability to assess model conceptual design, backtesting of model results, assumptions, controls over data flows, model execution, and compliance of model results with intended application.
Must possess a deep understanding of regulatory guidance and expectations in Model Risk Management (e.g., SR 11-7, SR 15-19).
Advanced programming skills in a statistical programming language, such as SAS, R, or Python.
Ability to independently write computer code to perform analysis on complex modeling and analytical challenges and to review code written by others for accuracy and efficiency.
Subject matter expertise in advanced mathematical and statistical modeling techniques, such as competing risk logistic regression, time series analysis, ordinary least squares, Monte Carlo simulation, and machine learning techniques (e.g. XGBoost).
Demonstrated ability to manage a team of technical professionals responsible for computer programming, including statistical or machine learning-based model development and validation.
Excellent oral and written communication skills.
Experience writing and reviewing detailed technical validation reports and/or model development documentation.
Strong attention to detail and the ability to independently formulate solutions to complex modeling and analytical challenges without existing procedures or precedent.
Ability to mentor staff through complex analytical challenges and difficult technical conversations with internal and external parties.
Ability to evaluate model risks, weigh pros and cons of risk mitigation, and communicate very technical concepts in plain language.
Perform job functions independently with minimal day-to-day oversight from supervisor.

Nice To Haves

PhD in quantitative discipline
Experience managing teams of technical individual contributors and/or external vendors performing technical modeling work
Experience in financial services or consulting industry
Experience developing or validating models used for CECL, Credit Risk, CCAR/Stress Testing, PPNR, ALM, loan pricing and/or mortgage servicing rights, derivatives, Compliance (BSA/OFAC), Liquidity, or Fraud
Subject matter expertise in generative large language models (Artificial Intelligence)
FRM or CFA certification

Responsibilities

Manage a team of model risk analysts / data scientists responsible for executing model validation activities across the model life cycle including model development, model validation, ongoing performance evaluation, and tracking model findings to ensure models across SECU are conceptually sound relative to their intended use and performing appropriately. Engage external consultants to perform validation work when necessary to include scope of required work, contract review and negotiation, and end-to-end management of vendor relationships.
Provide effective challenge to model assumptions, mathematical formulations, and implementations across a range of model architectures and use cases including competing risk logistic regression, time series analysis, ordinary least squares, Monte Carlo simulation, generative large language models (Artificial Intelligence), and machine learning techniques (e.g. XGBoost).
Document, rank, and effectively communicate technical modeling concerns to various audiences including model owners, senior leaders, and management committees.
Serve as a technical subject matter expert on the most complex code, data processing, and financial modeling challenges across the entire organization: credit risk, ALM, liquidity, fraud, BSA/AML, capital planning and stress testing, lending and loan pricing, cybersecurity, etc.
Develop and maintain effective partnerships within SECU to influence sound modeling practices, particularly with model owners, model developers, and data analysts. Represent SECU Model Risk Management in interactions with regulatory agencies and on SECU management committees.

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