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Stress Testing and Risk Appetite is a part of the Quantitative Risk and Stress Testing team with Citi Risk Management, and is broadly responsible for end-to-end internal stress testing, including firm-wide loss estimation for periodic (Global Systematic Stress Tests) and ad hoc (event-based) stress tests, and estimation and subsequent analysis of Risk Appetite metrics
The Head of Risk Appetite Metrics will help build out and lead the Risk Appetite Metrics team within the global Stress Testing and Risk Appetite team
The primary mandate of the newly created Risk Appetite Metrics team is to develop methodologies necessary to estimate firm-wide and business-specific stress losses, both those corresponding to specific stress scenarios as well as losses calibrated to a particular likelihood
The Head of Risk Appetite Metrics team will work closely together with the Scenario design team within Stress Testing and Risk Appetite, as well as with Corporate Risk Reporting to guide the estimation of losses and the assessment of Risk Appetite metrics for different businesses
The team's primary clients are Citi CRO Office and the Risk Management Executive council, as well as other senior managers within the Firm and external regulatory supervisors; as such, the Head of Risk Appetite Metrics will be expected to interact with senior Risk managers on a regular basis
Finally, the Head of Risk Appetite Metrics will be expected to help manage (as "matrix" reports) a growing number of Stress Testing and Risk Appetite team members in Dublin
Graduate degree (preferably PhD) in Finance, Economics or another quantitative field (Mathematics, Computer Science, etc.) is required
7+ years of experience in Quantitative Analysis of financial markets, Market Risk, or relevant academic research; must include experience across different asset classes
Demonstrated knowledge of modern quantitative finance, and particularly its practical applications. Experience in developing pricing or risk management models across asset classes is strongly preferred
Working knowledge of R and/or Python and modern principles of software development is required
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