Credit Risk Modelling

2 weeks ago

Gurugram Haryana, India Evalueserve Full time

**Elevate Your Impact Through Innovation and Learning**

We have 4,500+ talented professionals operating across 45 countries, including India, China, Chile, Romania, the US, and Canada. Our global network also extends to emerging markets such as Colombia, the Middle East, and the rest of Asia-Pacific. Recognized by Great Place to Work® in India, Chile, Romania, the US, and the UK in 2022, we offer a dynamic, growth-oriented, and open culture that prioritizes flexible work-life balance, diverse and inclusive teams, and equal opportunities for all.

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**Role Information**

Job Title Senior Analyst

Location Gurugram

Nature of Job Permanent

Department Risk and Quant Solutions

**About Risk and Quant Solutions (RQS)**

Risk and Quant is one of the fastest growing practices at Evalueserve. As an RQS team member, you will address some of the world’s largest financial needs with technology proven solutions. You would solve these banking challenges and improve decision making with award winning solutions.

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**What you will be doing at Evalueserve**

Important responsibilities in this role will include:

- Model development for Retail and wholesale model development, scorecard model development, IFRS 9/ CECL model development

**What we’re looking for**
- Excellent knowledge of credit risk models for the wholesale portfolio, model structure, variable treatments, variable data framework, and model development process.
- Extensive knowledge of statistical methods and tools such as logistic regression, Bayesian statistics, the Markov chain process, and time series analysis
- Knowledge of credit risk models - PD, EAD, and LGD, scorecards, IFRS 9, Basel II IRB approach for credit risk, logistic and linear regression models
- Extensive knowledge of SAS, Python (including OOP), statistical procedures, and automation capabilities in SAS and Python
- Understanding of various credit risk model constructs and credit risk modeling frameworks
- Understanding of the consumption layer of the curated data, i.e., model development for credit risk models, especially IFRS9, stress testing, and AIRB (PD, LGD, and EAD models)

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