● Performed Model Validation of 20 Excel, R, and Python-based quantitative and qualitative capital, pricing, and investment proprietary and vendor models in compliance with SR11-7 guidance and client MRM frameworks● Implemented backtesting, benchmarking, sensitivity, and stress testing under CCAR, CECL, and other regulatory scenarios to identify limitations and recommend mitigation controls for 20 banking and hedge fund client models● Identified issues in over 50 client workflows to develop analytics, and KPIs for risk monitoring in Python and Excel and recommended optimization and automation solutions● Assessed model eligibility, and conducted Risk Tiering of over 50 banking client models to assess conceptual soundness, suitability, governance, performance, and compliance● Drafted and delivered 20 validation reports with audit outcomes including recommended ongoing monitoring strategies and revised annual model review policies and reporting templates● Led weekly meetings with model developers, owners, internal auditors, key stakeholders, and executive management to perceive expectations, resolve implementation queries, and discuss critical findings● Reviewed over 100 model development and related documents to ensure models capture key business elements and risks, and interpreted regulatory rules and guidance related to the models under development and validation● Coordinated validation progress with leadership and led teams of 2-3 to achieve aggressive turnarounds

● Monitored and reported safety and security-related emergencies at the facility● Provided information and assistance for housing-related and general inquiries● Managed student packages and deliveries● Performed apartment safety inspections

● Researched factors affecting stock price and trading strategies, and consolidated related data for 100+ companies● Executed Time Series and Cross-Sectional analysis of over 100K data points to observe historical price trends and assess the impact of financial attributes and macroeconomic factors on company stock prices● Implemented Linear Regression, Support Vector Machine, XGBoost, and Neural Network machine learning models with identified features to forecast stock prices over a 1-month horizon and generate portfolios in Python● Developed quantitative metrics to monitor, verify, and compare the weekly performance of all models, devised adjustments to the model algorithm, and chose the best-fitting model● Conducted monthly model validation for process improvement and redevelopment to incorporate the latest data● Presented model-generated portfolios to business leads and retrieved feedback through monthly collaborations● Drafted and maintained a model development document with monthly performance and audit assessment results