Credit Risk Analyst
Published date: 2024/01/10
- Location: Mumbai, Maharashtra, India
Job Profile :
• Credit Risk Analytics and Modelling – Analyse, model, validate and document various measures of Credit Risk for use in Expected Credit Loss and Capital computations.
• Hands-on experience in building, implementing, documenting, monitoring, validating, refining models and scorecards – in particular for PD, LGD, EAD and related Credit Risk metrics - using statistical methods and / or expert judgement based techniques
• In depth understanding of the underlying statistical techniques and functional insights that drive Credit Risk models and the experience of having used modelled output in computing Expected Credit Loss (ECL) under Ind AS or Credit Risk Capital under Internal Ratings based approaches, Economic Capital and having taken these measures through the scrutiny of auditors and regulators
• Derive insights from data and effectively communicate these insights to Risk, Credit and Business teams
• Preparing and submitting reports to the regulator and / or senior management on a periodic and adhoc basis. The job-holder will be responsible for computing and reporting ECL and related metrics on an on-going basis.
• Ability to work with (gather, consolidate, prepare, summarise) large data sets on a routine basis with an eye for detail and flair for numbers. Data cleansing and preparation for use in model building.
• Ensure reliability, integrity and consistency of reported and underlying data
• Build and manage a team of like-minded data analysts and modellers
Requirements Job Requirements
• Between 6 and 9 years of experience in the areas of risk and data analytics – particularly in the area of Credit Risk. Exposure to risk reporting or financial reporting would be an advantage.
• Degree in statistics or econometrics (or equivalent professional qualifications) with hands-on experience in statistical model development, implementation, validation and use is highly desirable
• Extensive experience and expertise in using any one or more among Python / SAS / R. Intermediate level SQL skills will be useful.
• Strong experience in areas like delinquency or loan loss forecasting, expected credit losses capital computation under advanced approaches.
• Experience in projects involving automation / data consolidation in the areas of risk or financial reporting would be an advantage
• Credit Risk Analytics and Modelling – Analyse, model, validate and document various measures of Credit Risk for use in Expected Credit Loss and Capital computations.
• Hands-on experience in building, implementing, documenting, monitoring, validating, refining models and scorecards – in particular for PD, LGD, EAD and related Credit Risk metrics - using statistical methods and / or expert judgement based techniques
• In depth understanding of the underlying statistical techniques and functional insights that drive Credit Risk models and the experience of having used modelled output in computing Expected Credit Loss (ECL) under Ind AS or Credit Risk Capital under Internal Ratings based approaches, Economic Capital and having taken these measures through the scrutiny of auditors and regulators
• Derive insights from data and effectively communicate these insights to Risk, Credit and Business teams
• Preparing and submitting reports to the regulator and / or senior management on a periodic and adhoc basis. The job-holder will be responsible for computing and reporting ECL and related metrics on an on-going basis.
• Ability to work with (gather, consolidate, prepare, summarise) large data sets on a routine basis with an eye for detail and flair for numbers. Data cleansing and preparation for use in model building.
• Ensure reliability, integrity and consistency of reported and underlying data
• Build and manage a team of like-minded data analysts and modellers
Requirements Job Requirements
• Between 6 and 9 years of experience in the areas of risk and data analytics – particularly in the area of Credit Risk. Exposure to risk reporting or financial reporting would be an advantage.
• Degree in statistics or econometrics (or equivalent professional qualifications) with hands-on experience in statistical model development, implementation, validation and use is highly desirable
• Extensive experience and expertise in using any one or more among Python / SAS / R. Intermediate level SQL skills will be useful.
• Strong experience in areas like delinquency or loan loss forecasting, expected credit losses capital computation under advanced approaches.
• Experience in projects involving automation / data consolidation in the areas of risk or financial reporting would be an advantage
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