Contract Duration: 6+ Months

Required Skills & Experience

• Bachelor’s/University degree.
• 5+ years of experience in stress testing (CCAR/DFAST), CECL, or loss forecast model development.
• 5+ years of experience with data analytical tools like Python or R.
• Demonstrated experience of building analytical tools to support the analysis of loss forecasting results, using tableau, Excel, R shiny or Python
• Excellent quantitative and analytic skills. Ability to derive patterns, trends and insights, and perform risk/reward trade-off analysis.
• Knowledge on scenario design, sensitivity shocks and risk identification process.
• Proficient with MS Office suite, Word/Excel/PowerPoint.
• Good interpretations and communications skills to convey complex quantitative methodology in simple terms.

Desired Skills & Experience

• Master’s degree in economics, Finance, or quantitative majors.
• Sound knowledge of C&I and CRE loss forecast modeling analytics, PD/LGD/EAD models, experience in HFS/FVO.

What You Will Be Doing

• Execute monthly stress testing exercises to monitor WCR’s risk appetite and identify vulnerable areas.
• Cover key process of rapid stress testing, overlays.
• Provide analytics support to stress test models in wholesale products, connect the stress testing output to model drivers.
• Build tools & analytical capabilities to support outcome analysis, loss forecasting reports and what if analysis.
• Gather and analyze portfolio and macro-economic data to assess potential impact on business performance and integrate the trends to the portfolio loss forecast.
• 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.
• Research on 3rd party data, loss history and alternative models to build inventory of benchmarks.
• Contribute and refine current model performance monitoring process to interpret model output and identify opportunities for future improvements.