Quant Analytics Sr. Associate- Model Risk

11 hours ago

Chennai, India Tata Consultancy Services Full time

Role name: Quant Analytics Sr. Associate- Model RiskAs a Senior Quantitative Analytics Associate, you will be responsible for leading independent validations and reviews of the bank's various risk models. This role ensures that models are functioning as intended, comply with regulatory requirements, and that their risks are accurately identified, measured, and reported to senior management. Specifically, you will be performing in-depth validations and reviews of new and existing models used across the bank, including those for fraud risk, compliance risk (such as AML, OFAC), and/or other areas.Essential Job FunctionsPerform hands-on quantitative model validation/review. This includes testing the model's conceptual soundness, data accuracy, methodology, and ongoing performance through techniques like backtesting, benchmarking, and stress testing, etc.Provide an effective challenge throughout the model validation/review to ensure that models are robust, and all assumptions and limitations are justified.Present findings, weakness and/or observations identified from the validation/review to model developers/owners and provide them with executable finding remediations.Prepare detailed validation reports and memos that document the validation approach, findings, and conclusions.Participate in internal audits and regulatory exams by presenting validation results and methodologies and assisting in the remediation of any audit or exam findings.Act as a subject matter expert on modeling techniques, risk management practices, and regulatory trends. This involves performing research and developing advanced analytical tools or benchmarking models to aid the validation process.Required QualificationsMasters degree (or its equivalent) in a quantitative discipline and at least 3 years of relevant experience; or Bachelor’s degree (or its equivalent) and at least 5 years of relevant experience.Hands-on experience in statistical and AI/ML model development or validation, with a strong understanding of quantitative modeling methods (including AI/ML algorithms) used for various risk predictive models, such as fraud risk, AML risk models, etc.Proficiency in programming languages such as Python, R, SQL or SAS.Excellent written and verbal communication skills to clearly articulate complex technical findings to both technical and non-technical stakeholder.Knowledge of model risk management policies, procedures, and relevant regulatory guidance (e.g., from the OCC).

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