Role Overview:

The ideal candidate should have experience in Credit Risk Model Development or Credit Risk Model Validation in MNC Banks, Private Sector Banks, Foreign banks, Public Sector banks, or Consulting firms.

Credit Risk Methodology:

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

Quantitative Background:

Strong quantitative background in Applied Statistics, Mathematics, Operations Research, Economics, Engineering, or related quantitative fields.

Regulatory Regimes:

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), or HKMA (Hong Kong).

Credit Risk Models:

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

Statistical Modeling:

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 such as ARIMA and ARIMAX would be an added plus.

Programming Proficiency:

Highly proficient in statistical tools and programming languages including Python, SAS, SQL, and R.

Data Skills:

Strong experience with data analysis, data visualization, and data mining techniques.

Reporting:

High quality report writing skills from either a model development or model validation perspective, factoring in regional regulatory guidelines, frameworks, and Standard Operating Procedures.

Analytical Skills:

Strong critical reasoning skills and analytical capabilities for analyzing models and related modeling and financial products analysis exercises.

Stakeholder Management:

Adept in stakeholder management with excellent oral and written communication skills as well as interpersonal skills.

Time Management:

Sound time management and multitasking skills.

Responsibility Areas:

- In-depth and end to end validation of Credit risk models using both classical statistical techniques and machine learning approaches. Models span various businesses of the bank including Consumer Finance, Personal loans, Mortgages, Micro-Finance, Small Business Banking, Credit Cards, Corporate Banking, etc. and include both acquisition and behavioral score cards.

- Collect and analyze large datasets to calibrate and validate credit risk models.

- Evaluate the creditworthiness of clients and businesses and predict potential losses.

- Collaborate with cross-functional teams including FLoD (Model Developers, Model Owners, Risk, Businesses), Bureau teams, etc. to opine on the utility of the credit risk models in business decision-making processes.

- Staying up-to-date with banking industry trends and global, regional, and local regulatory requirements.

Education:

- Bachelors or Masters degree in Quantitative Finance, Statistics, Operations Research, Economics, Mathematics, Engineering or related quantitative fields.

- Risk Management qualifications such as FRM, PRM, or CFA are desirable though not mandatory.

Technical Knowledge:

- Strong analytical, numerical research and problem solving skills.

- Knowledge of working on software such as Python, R Studio, SAS, SQL, Power BI, etc.

- Proficient with MS Excel and Excel Macro.

- Possess excellent interpersonal and communication skills with an ability to interact at various hierarchical levels, with specific orientation to stakeholder interests.

- Well organized with the ability to perform under stringent timeline pressures without compromising on the end result quality.

- Knowledge of financial products and quantitative modelling in the banking industry.