Credit Risk Role - Model Validation

5 - 13 Years

Mumbai /Navi Mumbai

Posted 1 month ago

#Statistical Modeling #Model Validation #Credit Risk #Loss Given Default #Exposure At Default #Probability of Default #Risk Modeling #Statistics #SAS #SQL #Risk Analytics

1. Strong experience and practical in-depth understanding of Credit risk model development/validation methodologies and procedures.

2. Strong quantitative background in Applied Statistics/Mathematics/Operations Research/ Economics /Engineering / or related quantitative field.

3. Strong work experience and practical understanding of at least one or more of the following regulatory regimes: US (FRB/OCC), UK (PRA/ECB), CBUAE (MENA), RBI (India), MAS (Singapore), HKMA (Hong Kong).

4. Strong work experience and/or in-depth practical understanding of Credit Risk models - PD (Probability of Default), EAD (Exposure in Default), LGD (Loss Given Default) models from either model development or model validation standpoint.

5. Sound work experience and good practical understanding of Statistical modeling techniques of Linear Regression, Logistic Regression; Machine learning approaches of Gradient Boosting (GBM), XGboost (Extreme Gradient Boosting), Cat-Boosting, and Random Forest. Time Series modeling knowledge approaches - ARIMA, ARIMAX would be added plus.

6. Highly proficient in statistical tools/ programming languages (viz. Python, SAS, SQL, R).

7. Strong Experience with data analysis, data visualization, and data mining techniques.