Advanced Risk Model Validation Specialist

1 week ago

alwar, India beBeeQuantitative Full time

Job Title: Senior Quantitative Analytics Associate As a senior quantitative analytics associate, you will lead independent validations and reviews of risk models to ensure they are functioning as intended, comply with regulatory requirements, and accurately identify, measure, and report risks to senior management.The role involves performing in-depth validations and reviews of new and existing models used across the organization, including those for fraud risk, compliance risk, and other areas. You will test model soundness, data accuracy, methodology, and ongoing performance through techniques like backtesting, benchmarking, and stress testing.Perform hands-on quantitative model validation/review, including conceptual soundness, data accuracy, methodology, and ongoing performanceProvide an effective challenge throughout the model validation/review to ensure robust models with justified assumptions and limitationsPresent findings, weakness and/or observations identified from the validation/review to model developers/owners and provide executable remediationsPrepare detailed validation reports and memos documenting the validation approach, findings, and conclusionsParticipate in internal audits and regulatory exams by presenting validation results and methodologies and assisting in the remediation of any audit or exam findingsAct as a subject matter expert on modeling techniques, risk management practices, and regulatory trends, performing research and developing advanced analytical tools or benchmarking models to aid the validation processRequired Qualifications:Master's degree (or equivalent) in a quantitative discipline and at least 3 years of relevant experience; or Bachelor's degree (or equivalent) and at least 5 years of relevant experienceHands-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 modelsProficiency in programming languages such as Python, R, SQL, or SASExcellent written and verbal communication skills to clearly articulate complex technical findings to both technical and non-technical stakeholdersKnowledge of model risk management policies, procedures, and relevant regulatory guidance

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