Lead Quantitative Model Validator

1 week ago

solapur, India beBeeModelRisk Full time

Senior Quantitative Analytics AssociateWe are seeking an experienced Senior Quantitative Analytics Associate to join our team. As a key member of our risk management department, you will be responsible for leading independent validations and reviews of our 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.Key Responsibilities:Perform 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, weaknesses, 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.Requirements:Masters 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 stakeholders.Knowledge of model risk management policies, procedures, and relevant regulatory guidance (e.g., from the OCC).

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