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Quantitative Researcher - Concentration Risk Analytics (VP, TX)

Key Responsibilities:

Enterprise 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 at Citi, including both scenario design and firm-wide loss estimation for both periodic (Global Systematic Stress Tests) and ad hoc (event-based) stress tests.

Quantitative Researcher - Concentration Risk Analytics will join and help further build out the Concentration Risk Analytics team within Enterprise Stress Testing. The mandate of the newly created Concentration Risk Analytics team is to develop relevant methodologies and conduct firm-wide "inter-risk" stress testing exercises spanning multiple asset classes and risk stripes with the primary purpose of concentration risk identification. The team will be responsible for quantification of most pressing risks faced by Citi and assessment of firm-wide stress losses under relevant stress test scenarios. The stress testing process is expected to be robust and repeatable, and therefore will be generally model-driven and rely heavily on automation of its activities to the extent possible.

Quantitative Researcher - Concentration Risk Analytics is expected to contribute to designing any necessary software (as well as leveraging existing Citi infrastructure to the extent possible) to facilitate stress testing across a variety of risk types.
The team will work closely together with other teams within Enterprise Stress Testing as well as with Corporate Risk Reporting to apply stress scenarios to live risk exposures and guide the estimation of losses under each scenario. The team’s primary clients are Enterprise Concentration Risk Management team, as well as other senior managers within the Firm and external regulatory supervisors; as such, Quantitative Researcher - Concentration Risk Analytics will occasionally be expected to present and discuss his/her analyses with senior Risk managers



  • Graduate degree (preferably PhD) in Finance, Operations Research or another quantitative field (Computer Science, Mathematics, etc.) is required
  • 2+ years of experience in either sell-side or buy-side Research, Macro/Cross-Asset Trading or relevant academic research; preferably including experience across different asset classes
  • Experience in developing macroeconomic/market-driven portfolio loss models and familiarity with relevant regulatory guidance, including the CCAR/DFAST process
  • Strong working knowledge of R and/or Python and modern principles of software development is required


Grade :All Job Level - All Job FunctionsAll Job Level - All Job Functions - US


Time Type :Full time


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