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.