Manager-Model Risk Management

Overview

Hybrid model opportunity in Fort Worth, TX: Potential to work 3 days remote and 2 day onsite.

 

Why apply your data science skills in Model Risk Management?

 

Do you want to take your advanced modeling and data science acumen to the next level? The Model Risk Management team's mission is to be highly valued as experts in the latest modeling and AI/Machine Learning techniques and in best practices for addressing model development, maintenance, and deployment risks across diverse enterprise-wide businesses and functions.  No team has deeper knowledge exposure to as broad a range of essential models.  We directly contribute to the success of GM Financial by working with model owners from various areas of the company to examine the quality of comprehensive aspects of key models and by building advanced challenger models to add further value and insight.  We believe the crux of our mission boils down to adding highly technical value, cultivating relationships, and earning trust. That's why GM Financial needs passionate, innovative, and spirited team members just like YOU.   

 

Responsibilities

About the Role:

The Manager - Model Risk Management is the subject matter expert with an in depth knowledge of quantitative modeling methods, data sources and tools. The Manager brings a strong ability for independent learning and is a technical expert in the latest advances in modeling and model Risk Management and industry best practices.. The Manager leads a team charged with ensuring the success of critical model validation projects for the company's highest priority models, and with championing the model governance framework across the enterprise while maintaining the corporate model inventory, the model risk technological tools, and by furnishing key information to Enterprise Model Governance council.

 

In this role you will:

• Lead, develop and coach a team of Model Risk Data Scientists.
• Collaborate with third parties and model owners to realize the effective challenge and validation of advanced statistical, predictive, prescriptive and Artificial Intelligence (AI)/ Machine Learning (ML) models.
• Educate stakeholders on model governance policies, procedures, and best practices.
• Provide appropriate reporting to risk committees, internal audit and regulators.
• Monitor KPI's and provide recommendations to resolve model risk exposure to leadership across the organization.
• Facilitate timely resolution of risks identified during model validations.
• Collaborate with various stakeholders including Model Owners, Legal, Privacy, Financial Assurance, Cybersecurity, Vendors, etc.
• Ensure accuracy and completeness of reporting and presentations communicated to stakeholders.
• Maintain the corporate model inventory and key model Risk Management technological tools.
• Research latest trends, emerging statistical and machine learning methodologies and technologies to facilitate education and sharing of model practices across the organization.

Qualifications

What makes you a dream candidate?

• Advanced knowledge and demonstrated understanding of applied methodologies including least squares regression, logistic regression, sampling methodologies, time series, survival analysis, cluster analysis, categorical data analysis, decision trees, multivariate methodologies, non-parametric techniques, principal components, optimization, simulation, and AI/ML modeling techniques.
• Demonstrated ability to identify and understand business issues, examine modeling problem formulation, and interpret their mapping into the quantitative modeling solutions and business benefits during resolution.
• Proven experience in model conceptualization, development, testing, documentation, monitoring and ongoing maintenance of advanced statistical and machine learning models.
• Familiarity with specific statistical and AI/ML Python libraries, such as NumPy, MatPlotLib, Pandas, SciPy, Scikit-learn, Tensorflow, Keras and LightGBM.
• Familiarity with AI/ML model explainability/interpretability toolkits for enabling explainable models involving decision trees, Random Forest, XGBoost, LightGBM, etc.
• Knowledge of data query languages like SQL, and of cloud-based MS Azure databricks use in modeling.
• Comprehensive knowledge and experience with technical systems, datasets, data warehouses, data lake, and data analysis techniques.
• Advanced quantitative, analytical and data interpretation skills with a solid foundation of in mathematics, probability, statistics, and overall emerging AI/ML methodologies.
• Strong written and verbal presentation skills with an ability to communicate effectively with Senior Management by making complex concepts easy to understand.
• Strong acumen for model documentation and report writing/comprehension.
• Strong analytical, critical thinking and problem-solving skills, including interviewing, Lean Development, Agile, appreciative inquiry, and ladder of inference.

Experience

• Bachelor's Degree Finance, Economics, Mathematics, Business, Business Analytics, MIS, or other quantitative field required
• Master's Degree preferred
• 5-7 years Experience building financial or credit models, conducting model validation, working with complex Excel workbooks, data analysis, and data presentation; need proficiency with the following tools: SAS and/or SQL, Microsoft Excel, PowerPoint, and Word required
• 3+ years' experience in the Financial Services Industry preferred

What We Offer: Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays.Our Culture: Our team members define and shape our culture - an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work - we thrive.Compensation: Competitive pay and bonus eligibilityWork Life Balance: Flexible hybrid work environment, 2-days a week in office

#LI-Hybrid

Employment Type: FULL_TIME