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Specialized credit-risk plus GCP/SAS/LLM skills narrow applicants, but metro location raises interest.
Requires banking credit-risk expertise and regulatory modeling knowledge, limiting cross-industry transferability.
Mandatory credit-risk modeling domain knowledge plus GCP/SAS/R/Python and AI tooling create moderate filtering.
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Design, build, and deploy AI tools and LLM applications to support credit risk analytical modelers.
Develop and validate credit risk models using GCP, SAS, R, and Python.
Translate modeling outputs into actionable business insights and prepare documentation and presentations for credit risk modeling process.
Master's degree in Finance, Financial Engineering, Analytics, Mathematics, Computer Science, Statistics, Industrial Engineering, Operations Research, or related field.
Experience in credit risk modeling techniques including Probability of Default (PD), LGD, and EAD modeling.
Proven hands-on experience in Artificial Intelligence and extensive experience with Google Cloud Platform (GCP).
Programming skills in GCP, R, SAS, and Python.
Strong proficiency in predictive and statistical modeling techniques and their business applications, especially within credit lifecycle analytics.
Experience developing cloud-based analytical solutions on platforms like GCP with a focus on scalability and operational efficiency.
Ability to collaborate closely with modelers and translate complex analytical challenges into AI-powered solutions while ensuring compliance with data privacy and security standards.