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Tiger Analytics
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Quantitative Risk and Model Validation Analyst

Quantitative Risk and Model Validation Analyst

Tiger Analytics

3 - 7 years

0 Lacs

hyderabad telangana

Posted:1 week ago| Platform: Shine logo

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Skills Required

econometrics statistics regression analysis time series analysis optimization risk models optimization algorithms sas python sql machine learning exploratory data analysis quantitative methods

Work Mode

On-site

Job Type

Full Time

Job Description

Role Overview: You will be joining Tiger Analytics, a global leader in AI and analytics, in the role of Quantitative Technical Advisor. In this position, you will be responsible for designing and validating mathematical models used by the bank, specifically focusing on Market Risk and Counterparty Credit Risk division. Your role will involve analyzing and building sector/industry risk assessment mathematical methodologies and quantitative measurement techniques for assessing credit risk exposures and evaluating structured products. Key Responsibilities: - Monitor and validate aggregate model risk in alignment with the bank's risk strategy - Lead a team of Model validators who review and validate various models using predictive and AI Modeling knowledge - Manage a growing Model Validation team responsible for independent first-line validation of predictive and generative AI models - Perform independent validations of financial, statistical, and behavioral models based on their criticality ratings - Assist in validating and reviewing models for theoretical soundness, testing design, and identifying weaknesses - Interpret data to identify potential risk exposure - Develop challenger models to validate existing models and assist with outcome analysis - Ensure compliance with the model risk monitoring framework - Evaluate governance for Model Risk Management by reviewing policies, controls, risk assessments, documentation standards, and validation standards - Preferably have exposure to CCAR/CECL/IRB Models - Evaluate the Bank's compliance with SR 11-7 regulatory guidance Qualifications: - Graduate/Master's degree in computer science, statistics, econometrics, mathematics, computational finance, or similar field - Deep knowledge in Quantitative methods, econometrics, and statistics - Proficiency in Regression analysis, Time series Analysis, and optimization - Understanding of Risk Models and Optimization algorithms - Good working knowledge of SAS, Python, SQL, Machine learning, and exploratory data analysis,

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Quantitative Risk and Model Validation Analyst