Citi Group MRM – C09- Balance Loss Forecasting Model Validation 

India, Haryana, Gurugram 

570889557

• Model Risk Management (MRM) is an independent oversight function. The Mumbai center is one of the six MRM locations across the globe and is responsible for development and maintenance of Model Risk Management Policy and procedures, for evaluation and approval of very high, high and medium high risk models used in global consumer risk management.

  The position will be part of the Model Risk Management, India team. His/her primary function is to conduct validation activities for Consumer risk scoring & segmentation models, machine learning models and loss forecasting, balance forecasting, stress testing, PPNR and macro-economic forecasting models that are developed by other Global Consumer Teams for regulatory submissions such as Comprehensive Capital Analysis and Review (CCAR). The roles are very critical to the organization, as MRM’s authorization on the use of the models are based on the reviewer’s evaluation results. The reviewer will adhere to the Model Risk Management Policy when evaluating models and ensure models, documentation, and monitoring MIS are compliant with applicable policies.

  Key Responsibilities:

• Review and validate new and existing model and framework, provide effective challenge, ensure validation work quality.
• Help manage model risk across the model lifecycle including model validation, performance evaluation and annual model reviews.
• Provide effective challenge to the model design and construction and conduct incremental analysis and testing as necessary
• Challenge and continually improve MRM Guidance on modeling approaches and model performance testing.
• Contribute to strategic, cross-functional initiatives within MRM organization.

• There will be plenty of learning and growth opportunities with this position, from both technical and leadership perspective. The incumbent will be exposed to different areas of business operations and a variety of modeling approaches, including machine learning on top of industry standard tools.

  Qualifications
• Minimum of Master’s degree in a quantitative field (Statistics, Mathematics, Physics, Engineering, Computer science, etc.)
• Higher academic qualifications and/or certifications such as a PhD, a second Master’s degree, CPA, CFA or FRM is a plus
• Must have a strong background in statistical modelling techniques.
• Good understanding of Model Risk Management.
• Strong written and oral communication skills.

Time Type:

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