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This role is part of the Credit & Capital Management team and supports development and maintenance for all models in the function. The candidate will find an understanding of regulatory guidance on model risk management helpful.
The role will aid management of models through their lifecycle, from development phase including testing, through to implementation and into production. This will entail writing code to perform data and statistical analysis, defining various model performance metrics, performing development testing and supporting documentation efforts etc. The successful candidate will complement his/her work with well written documentation.
This is a great opportunity for an experienced Python/Spark programmer with experience in big data, credit and/or financial analysis along with experience in statistical analysis and ideally machine learning. We offer a dynamic, collaborative team environment with a strong risk management culture.
Aid management of models through their lifecycle, from development phase through to implementation into production. Meet project timelines, engage in ongoing analysis, evaluate alternative methods, find performance thresholds, perform development and unit testing and support documentation efforts etc.
Interface with validators and actively support validation efforts.
Coordinate the seamless delivery to model users in their production environment.
Adhere to and follow model governance standards.
Observe and check model outputs; coordinate investigations to understand drivers of their performance. Outline enhancements for evaluation and future model releases.
Ensure model development and ongoing monitoring documentation is completed to meet model validation standards, including key decision points and their rationale.
Report on Model status and development progress.
Keep model information for owners: model version control, performance assessments, opportunities for improvements, re-calibrations and re-fits when business dynamics change.
Perform other duties and/or special projects as assigned
2+ years of experience in Risk, Credit, Finance, Consumer Lending, or other relevant professional experience; in lieu of a degree 6+ years of experience in Risk, Credit, Finance, Consumer Lending, or other relevant professional experience
2+ years of experience in credit loss and receivable modeling in areas such as Loss Forecasting, ALLL/CECL, Stress Testing, or other areas with consumer credit performance estimation
2+ years of experience using Python / Spark / SAS / SQL to perform statistical analysis, query relational databases, and handle large amounts of data
2+ years scripting, HDFS and Git experience to execute version control and simple file management tasks
Graduate degree with quantitative underpinning, MBA, CFA or other relevant certifications or designations
Experience with ALLL, Loss Forecasting and / or Receivable forecasting, specifically related to credit card / consumer lending
Software development / agile experience
Familiarity with Model Governance trends/developments across the banking sector
Problem solving skills: Strong ability to rapidly learn the intricacies of an unfamiliar process, structure and scope complex problems, apply a range of analytical tools, gain and synthesize insights and develop actionable recommendations
Writing skills to document technical, complex discussions succinctly