Citi Group VP Wholesale Stress Testing Hybrid 

United States, New York, New York 

70811096

Responsibilities:

• Perform quarterly gap assessment between scenario coverage and material risk inventory.
• Communicate and present the impact of scenario changes on model outputs to senior business leaders
• Execute periodic stress testing exercises to monitor WCR’s risk appetite and identify vulnerable areas.
• Provide analytics support to explain the stress test outcome for wholesale lending products.
• Partner with business units and risk managers to assess data availability and fit for purpose modeling approaches.
• Interact with model developers, model risk governance, business risk, internal audit.
• Leverage business / product expertise to evaluate and challenge the stress loss assumptions in hypothetical and historical stress scenarios.
• Gather and analyze portfolio and macro-economic data to assess potential impact on business performance and integrate the trends to the portfolio loss forecast.
• Research on 3rd party data, loss history and alternative models to build inventory of benchmarks.
• Develop deep expertise in stress testing methodologies and validate fit for purpose usage in BAU stress testing management.
• Contribute and refine current model performance monitoring process to interpret model output and identify opportunities for future improvements.
• Create a new scenario design capability leveraging existing models & data to translate emerging risk to economic scenarios, model inputs or portfolio shocks.
• Build tools & analytical capabilities to support outcome analysis, loss forecasting reports and what if analysis.
• Works with large datasets and complex algorithms to derive analytical insights, identify data quality issues and support trend analysis.
• Leverages big data to develop innovative deployable solutions.

Qualifications:

• 5+ years’ experience working in financial institutions.
• Sound knowledge of statistical modeling concepts and industry best practices; experience with econometric and statistical modeling or application risk scoring.
• Excellent quantitative and analytic skills; ability to derive patterns, trends and insights, and perform risk/reward trade-off analysis.
• Experience with analytical or data manipulation tools(e.g. Python, Tableau, R)Proficient with MS Office suite.
• Past experience working on model analytics, back testing, benchmarking and challenger function.
• Knowledge on scenario design, sensitivity shocks and risk identification process
• Good interpretations skills to convey complex quantitative methodology in simple terms.

Education:

• Bachelor’s/University degree or equivalent experience, potentially Masters degree

Anticipated Posting Close Date:

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