AVP - Independent Model Review|
Markets IMR is a global quantitative team responsible for independent review and approval of the Front Office models required for official reporting purposes, traded risk models (Market Risk and Counterparty Risk), Balance Sheet Management models, Asset Liability and Capital Management (ALCM) Models, and Asset Management models. The members work closely with Front office, desk quants, risk managers, IT developers, etc.
This specific position will work in the New York/New Jersey Markets IMR team, with a focus on Asset Liability and Capital Management (ALCM) Models and Balance Sheet Management (BSM) Models.
- Primary job is to review and validate global ALCM and BSM models including fixed income product models, interest rate risk models, funding and capital management models, liquidity risk models and currency risk models. Examples of model coverage include but not limited to yield curve models, interest rate models, derivative pricing models, deposit balance models, deposit offering rate models, mortgage prepayment models and default models, cash flow models, EVE/NII models, MBS/ABS models, PPNR models. May also review and validate Global Market valuation models, asset management models, market risk models and counterparty risk models.
- Review and provide quantitative assessment on the models including their theoretical soundness, assumption, limitations, consistency, stability, and calibration. Model risk assessment is the focus.
- Perform Asset/Liability process verification and model benchmarking; Justify the model assumptions/constraints and assess their impacts to the valuation and risk by performing sensitivity analysis and stress testing on models as required;
- Validate the implementation of the model in library, risk engines, or systems, and writing appraisal reports.
- Work with senior members of the model review group to develop independent quantitative library for model risk assessment and model implementation validation purposes.
- Perform quantitative/mathematical/statistical assessment procedures necessary to validate model parameters and performance;
- Keep up to date with academic, technical and industry developments in model design, development, validation and stress testing, data visualization and interpretation, and regulatory requirements in model risk assessment and control;
- Apply advanced analytics such as artificial intelligence and machine learning to develop independent benchmarking models
- Advise risk units or business units on the most appropriate quantitative estimation, validation and stress testing methodologies to use;
- Provide model related support and ad-hoc analysis.
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Qualifications & Requirements
- Strong background in mathematical finance, statistical analysis, and derivative pricing models. 1-5 years of quantitative experiences in model development or model validation. Fresh smart Ph.D. from top schools without prior experience will be considered if a big learning potential is demonstrated.
- Excellent verbal and written communication skills. Must be able to explain complex and/or technical matters clearly, accurately and simply. Need daily interaction with senior management, business heads, traders, desk quants, model review team members, market risk and credit risk managers, product controller, and senior IT developers.
- Organized, detail-oriented and self-motivated. Work hard, smart, productive, business driven, be able to delivery under pressure. Ability to work in a team.
- Regulatory stress testing experience (such as CCAR/DFAST) is a plus.
- Experience in statistical modelling is required, together with knowledge in data science fields such as big data, cloud computing, databases, visualization, and machine learning. Knowledge and/or experience in MBS/ABS, mortgage prepayment/default modelling, deposit behavioralization modelling is a plus.
- Solid knowledge and experience with Data and Analytics Tools/Languages such as R, Python, SAS, SQL, Spark, Hive, Tableau, Google Analytics, Amazon Web Services.
- PhD in a quantitative or computational filed (Finance, Maths, Statistics, Physics, Financial Engineering, Computer Science) is preferable, but very capable MS in these fields with relevant experience (3 years) may also be considered.