Senior Quantitative Analytics Expert

2 weeks ago

india beBeeRisk Full time

Job Title:Quantitative Risk Modeling ProfessionalThis role involves leading independent validations and reviews of various risk models, ensuring they function as intended, comply with regulatory requirements, and accurately identify, measure, and report risks to senior management.The successful candidate will perform 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 other areas.Perform hands-on quantitative model validation/review, including testing the model's conceptual soundness, data accuracy, methodology, and ongoing performance through techniques like backtesting, benchmarking, and stress testing.Provide an effective challenge throughout the model validation/review to ensure that models are robust, and all assumptions and limitations are justified.Presentation of findings, weaknesses, and observations identified from the validation/review to model developers/owners, along with executable finding remediations.Preparation of detailed validation reports and memos that document the validation approach, findings, and conclusions.Participation 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, involving research and development of advanced analytical tools or benchmarking models to aid the validation process.Required Qualifications:Master's 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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