Manager-Model Risk Management

hace 5 días

Partido de Dolores, Argentina GM Financial A tiempo completo
Overview

Why GM Financial?

GM Financial is the wholly owned captive finance subsidiary of General Motors and is headquartered in Fort Worth, U.S. We are a global provider of auto finance solutions, with operations in North America, South America, and the Asia Pacific region. Through our long-standing relationships with auto dealers, we offer attractive retail financing and lease programs to meet the needs of each customer. We also offer commercial lending products to dealers to help them finance and grow their businesses.

At GM Financial, 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.

Our Purpose: We pioneer the innovations that move and connect people to what matters.

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 of Model Risk Management analysts 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 the Enterprise Model Governance Council.

  1. Lead, develop and coach a team of Model Risk Management Analysts.
  2. 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.
  3. Educate stakeholders on model governance policies, procedures, and best practices.
  4. Provide appropriate reporting to risk committees, internal audit and regulators.
  5. Monitor KPI's and provide recommendations to resolve model risk exposure to leadership across the organization.
  6. Facilitate timely resolution of risks identified during model validations.
  7. Collaborate with various stakeholders including Model Owners, Legal, Privacy, Financial Assurance, Cybersecurity, Vendors, etc.
  8. Ensure accuracy and completeness of reporting and presentations communicated to stakeholders.
  9. Maintain the corporate model inventory and key model risk management technological tools.
  10. Maintain current information, forms and other model risk-related materials for stakeholders and model owners at restricted Sharepoint locations.
  11. Research latest trends, emerging statistical and machine learning methodologies and technologies to facilitate education and sharing of model practices across the organization.
  12. Foster an environment of continuous improvement and development for self and direct reports.
Qualifications

What makes you a dream candidate?

  1. 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.
  2. 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.
  3. Proven experience in model conceptualization, development, testing, documentation, monitoring and ongoing maintenance of advanced statistical and machine learning models.
  4. Familiarity with specific statistical and AI/ML Python libraries, such as NumPy, MatPlotLib, Pandas, SciPy, Scikit-learn, Tensorflow, Keras and LightGBM.
  5. Familiarity with AI/ML model explainability/interpretability toolkits for enabling explainable models involving decision trees, Random Forest, XGBoost, LightGBM, etc.
  6. Knowledge of data query languages like SQL, and of cloud-based MS Azure Databricks use in modeling.
  7. Advanced quantitative, analytical and data interpretation skills with a solid foundation of in mathematics, probability, statistics, and overall emerging AI/ML methodologies.
  8. Proficient in at least one or more of the following languages: Python, R or SAS.
  9. Strong written and verbal presentation skills with an ability to communicate effectively with Senior Management by making complex concepts easy to understand.
  10. Strong acumen for model documentation and report writing/comprehension.
  11. Strong analytical, critical thinking and problem-solving skills, including interviewing, Lean Development, Agile, appreciative inquiry, and ladder of inference.
  12. Detail oriented with excellent organizational and time management skills, and ability to manage multiple tasks/deadlines simultaneously.
Experience
  1. Bachelor's degree in Statistics, Data Science, Applied Mathematics, Econometrics, Finance, Operations Research, Industrial Engineering, Physics, Computer Science, or similar quantitative field required.
  2. Master's degree in Statistics, Data Science, Applied Mathematics, Econometrics, Finance, Operations Research, Industrial Engineering, Physics, Computer Science, or similar quantitative field required.
  3. 5+ years as a Data Scientist or similar quantitative field required.
  4. 1+ years in project leadership role required.
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 eligibility.

Work Life Balance:

Flexible hybrid work environment, 2 days a week in office.

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