Python Job: Quantitative Modeler

Job Type: Contract through the end of the year with opportunities for extensions or conversions

Location: Remote working EST hours - prefer some travel to Buffalo, NY or Charlotte, NC - expenses paid!

Summary:

Independently implements, executes, and analyzes quantitative/econometric models and their outputs used for credit risk, balance sheet, and income statement in support of capital planning.

Responsibilities:

• Develop, implement, and maintain CECL and CCAR models using Python and other relevant tools, ensuring accuracy, scalability, and efficiency.
• Lead the implementation of quantitative models used for credit risk, balance sheet, and income statement in support of capital planning, including but not limited to, loan delinquency, default and loss models, loan prepayment and utilization models, deposit attrition models, PPNR models and financial instrument valuation methods.
• Prepare, manage and analyze large customer loan, deposit, or financial data sets for statistical analysis in Structured Query Language (SQL) or similar tool to properly implement models and analyze model outputs to understand customer or Bank behavior for the purposes of capital adequacy assessment and capital risk management. Understand the context of the Bank s data and businesses to ensure properly developed models.
• Execute models in production environment; communicate analytical results to stakeholders. Track portfolio dynamics, model forecasts, and risk strategy results. Incorporate observations and data to improve operation efficiency.
• Develop, maintain and manage satisfactory implementation documentation, including process narratives, quality control and workflow guidelines to serve as reference source.
• Provide guidance and direction to less experienced personnel regarding all aspects of data and financial analysis and model implementation.

Requirements:

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, or a related field.
• Strong proficiency in Python programming language, with demonstrated experience in data analysis, modeling, and visualization.
• Experience working with SAS and cloud-based services, with a focus on model deployment and productionization.
• Familiarity with machine learning techniques and algorithms, including supervised and unsupervised learning, classification, regression, clustering, and feature engineering.
• Knowledge of banking regulations and compliance frameworks, particularly CECL and CCAR, is essential.
• Excellent communication skills with the ability to convey complex technical concepts to non-technical stakeholders.