Job Purpose Validate models in accordance with clients model risk management policy to assess model usage, documentation, conceptual soundness, data integrity and the control environment. Communicate results via formal model validation reports, as well as presentations to model owners and senior management. Evaluate model performance monitoring reports, and conduct model annual reviews. Maintain the model inventory and support the model risk governance process. Perform ad hoc (generally statistical) analysis of back-tests or simulated performance information. The Mark-to-Market Valuation (MTM) is used for valuations of trades that already exist for mark to market. The Credit Valuation Adjustment (CVA) accounts for measures of counterparty non-performance risk and is an adjustment made to the value of a financial instrument to reflect the possibility that a counterparty might default when the instrument is an asset for the sponsor. The Pricing and Potential Future Exposure model (PFE) produces distributions of rate or valuation outcomes based on expectations of future interest rates. It is used for pricing new customer trades for interest rate swaps before taking to market and in credit underwriting. Desired Skills and experience Masters degree in mathematics, statistics, data science, finance or a relevant field. Experience in model development, model validation or model governance in the fields of Credit Risk, Market Risk, Operational Risk, Fraud Risk. Programming experience with demonstrated exposure to multiple languages such as Python, SAS, R, MATLAB, SQL, VBA, C++ or similar languages. Experience required in building and pricing trades including Mark to Market Valuations, CVA models, Pricing and potential future exposure models Familiarity with vendor investment analytic applications: Aladdin, Axioma, Barra Portfolio Manager, Barra One, RiskMetrics, Yieldbook, Moodys Analytics, etc. Experience with vendor financial data vendors: Bloomberg, Refinitiv, CRSP, MSCI, Markit, S&P Capital IQ, etc. Solid understanding of financial predictive modeling, such as multi-factor risk models, time series forecasting, Value-At-Risk (VaR), optimization theory, and machine learning. Demonstrate strong understanding of capital markets and various asset classes: equities, fixed income, FX, and commodities. Attainment or progress toward at least one of the following: Graduate degree in business or quantitative discipline, Chartered Financial Analyst (CFA) charter, FRM or PRM risk management certifications, Certified Investment Management Analyst® (CIMA®) certification. Demonstrated history of strong analytical skills and attention to detail.
