Analyst II - Model Validation

Key Skills

Aartificial intelligence, Agile methodologies, Azure, Data science techniques, Databricks, Machine learning techniques, NumPy, Pandas, Python Programming, R Programming, Random forests, SAS, Scikit-learn, SciPy, SQL, XGBoost

Job Description

Do you want to take your advanced modeling and data science acumen to the next level? The Model Governance 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 Analyst II, Model Validation leverages an in-depth knowledge of quantitative modeling methods, data sources and tools. The Analyst II brings a strong ability for independent learning and is knowledgeable in the latest advances in modeling, model risk management and industry best practices. The Analyst II collaborates with a team of Model Validation Analysts charged with the independent effective challenge of models while championing the model governance framework across the enterprise.

In this role you will:

• Develop challenger models in Python as needed to validate the soundness and accuracy of existing models
• Have exposure and input to all models across GM Financial Enterprise
• Ensure the effective challenge 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
• Research latest trends, emerging statistical and machine learning methodologies and technologies to facilitate education and sharing of model practices across the organization
• Support an environment of continuous improvement and development

Qualifications

What makes you a dream candidate?

• 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 Lean Development, Agile, appreciative inquiry, and ladder of inference

Education and Experience

• Master’s Degree in Statistics, Data Science, Applied Mathematics, Econometrics, Finance, Operations Research, Industrial Engineering, Physics, Computer Science, or similar quantitative field required
• 2-4 years as a Data Scientist or similar quantitative field required
• Proficient in at least one or more of the following languages: Python, R or SAS

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