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About Tiger Analytics Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow. Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence. We are a Great Place to Work-Certified™ (2022-24), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine. About the role The position of Quantitative Technical Advisor will be designing and validating mathematical models used by the bank and conducting mathematical analysis of the data in the Market Risk and Counterparty Credit Risk division. Analyzing and building sector / industry risk assessment mathematical methodologies and quantitative measurement techniques for assessment of the credit risk exposures and evaluation of the structured products. Roles & Responsibilities: Monitor and validate aggregate model risk in alignment with bank’s risk strategy You will lead a team of Model validators, who use their predictive and AI Modeling knowledge to review and validate a wide variety of the models 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 commensurate with their criticality ratings Assist with the validation and review of models regarding their theoretical soundness, testing design, and points of weakness Interpret data to recognize any potential risk exposure Development of challenger models that help 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 Exposure to CCAR/CECL/IRB Models are preferred. Evaluate the Bank’s compliance with SR 11-7 regulatory guidance" Qualification: Education: Graduate/Master degree in computer science, statistics, econometrics, mathematics, computational finance, or similar Deep knowledge in Quantitative methods / econometrics/ statistics. Strong in Regression analysis, Time series Analysis, optimization. Knowledge of Risk Models and Optimization algorithms. Good working knowledge of SAS, Python and SQL, Machine learning, exploratory data analysis Show more Show less
