Quantitative Risk Analyst
1 month ago
DETAILS:
Domain: Banking
Position: Quantitative Risk Analyst
Duration: 6 Months (Possibility of extension or conversion)
Location: Irving, TX (Hybrid 3 Days/Week Onsite)
Key Responsibilities:
- 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
Other Responsibilities:
- 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
Qualifications:
- 5+ years' experience in stress testing (CCAR/DFAST), CECL, or loss forecast model development
- 5+ years' experience with data analytical tools like Python or R
- Sound knowledge of C&I and CRE loss forecast modeling analytics, PD/LGD/EAD models, experience in HFS/FVO is preferred
- 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.
- Proficient with MS Office suite, Word/Excel/PowerPoint.
- Knowledge on scenario design, sensitivity shocks and risk identification process
- Good interpretations and communications skills to convey complex quantitative methodology in simple terms
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