• Managed credit risk of the $115B investment portfolio, leveraging financial and statistical analysis techniques to optimize risk/reward• Automated reporting and validation using Python to simplify processes, reduce reporting time by 50%, and proactively manage risk• Proposed a Strategic Asset Allocation model for Life Company to maximize investment income while adhering to policy constraints• Developed and cascaded a real estate investment strategy to increase annual returns by $1.97M while minimizing expected losses• Delivered training to 300+ employees on investment credit risk, counterparty risk, and tail value-at-risk to enhance enterprise awareness• Built financial models in Python and Tableau to vary parameters and observe potential losses under current and stressed scenarios• Reviewed and challenged investment credit risk appetite metrics and key risk indicators for the 2025 annual metric refresh cycle• Conducted data analysis and visualization using Bloomberg, SQL, Python, and Tableau to make decisions within USAA risk appetite

• Facilitated modeling project to auto-approve 15% of deals without manual review in response to client feedback on urgency• Managed relationships with external stakeholders and internal teams to carry $70M of deals from origination through funding• Reduced pipeline reporting time by 400 minutes per month by consolidating management reports into a shareable format

• Assessed the financial strength of Fortune 500 companies to conduct cash flow lending for $1 – 5M sized equipment transactions• Constructed credit approval documents with financial statement analysis, borrower quality rating justification, and risk mitigation• Monitored existing counterparty exposures to identify material adverse financial changes that suggest borrower credit deterioration

• Practiced financial underwriting for commercial clients by analyzing quantitative and qualitative metrics in several case studies• Presented WF org structure, line of business knowledge, risk management framework, and credit approval process to leadership

• Extracted, compiled, and analyzed data sets to identify abnormalities and fraud during the Global Financial Crisis• Utilized Python, SQL, and R to generate datasets and visualize market trends in healthcare, banking, and cryptocurrency industries