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Our Client ranks among the world's largest banks and stands out as a premier provider of corporate and services for its clients.

They are currently looking for 3 - Quantitative Credit Risk Analytics - VP (Direct Hire) in New York, NY (Hybrid). They're looking for 5-7+yrs of experience in quantitative modeling for credit risk. Thorough grasp of of main credit risk parameters (TM/PD, LGD, EAD, and EL) modeling.

If the opportunity interests you, please send your resume and contact details to

Summary:
The VP-level quantitative credit risk analytics professional is tasked with developing methodologies and overseeing analytics for stress testing, risk appetite, CECL, economic capital, and counterparty models, including TM/PD, LGD, EAD, EL, and value-at-risk. The candidate will join the Credit Risk Analytics group, contributing to model development throughout its entire life cycle, from methodology design to local implementation and validation. They will lead risk analytics initiatives and project development.

Responsibilities:
Develop, test, implement, and document credit risk models and analytics.
Conduct quantitative research to enact model changes, enhancements, and remediation plans.
Collaborate with stakeholders across business and functional teams during model development and implementation.
Design tools and dashboards to enhance risk analysis capabilities.
Analyze existing model shortcomings and devise remediation strategies.
Maintain, update, and enhance existing models.
Identify risks not captured by analytics, quantify their materiality, and devise strategic plans for integration and management.
Support regulatory discussions as a subject matter expert.

Qualifications:
Masters Degree in a quantitative field; PhD preferred.
5-7 years of experience in quantitative modeling for credit risk.
Strong analytical skills for understanding quantitative models and translating insights into sustainable library design, code development, and IT system integration.
Proficiency in statistics and statistical tools, including hypothesis testing, regressions, time series models, MCMC Bayesian tools, and state space models.
Deep understanding of key credit risk parameters (TM/PD, LGD, EAD, and EL) modeling.
Proficient programming skills in Python; familiarity with R and SQL is beneficial.
Experience with machine learning techniques applied to risk modeling is advantageous.
Strong project management and organizational abilities.
Excellent writing and presentation skills.
Superior oral and written communication skills, with the ability to effectively communicate complex concepts to non-quantitative managers.