1. Lead validator for Capital One’s US$ 60B CRE and REIT/REIF portfolio Commercial PD model suite:Key use: Credit strategy/Credit decision, Loan pricing, Capital planning, and Stress testing.– Awards: ’Achiever’s Award’ for exceptional improvement in the model performance by proposing a new modeling methodology. 2. Lead validator of Capital One’s retail bank deposit model suite covering a portfolio of total deposits with $ 296B:Key use: Deposit forecasts/outlooks, NIM predictions, and interest rate decisions.– Conducted quality assessment for data sources, conceptual soundness of methodology, stress testing, and deployment.– Awards: ’Statistically Significant Award’ for highlighting key risks in the model performance. Developed Benchmark models using insights from the latest industrial trends and built on identified model weaknesses:– Key achievements: Improved model by introducing Occupancy Rates and inflationary adjustments to improve model rank ordering by 12%.3. Designed and developed an automation tool for Commercial LGD models’ quarterly monitoring with a divisional-level impact and promoting ease of risk identification with ongoing risk management activities.4. Models handled in the Bank fraud space: Debit Card Authorization model, Zelle payment fraud model, Account Takeover Model. Models are largely based on methodologies such as XGBOOST/GBM on big data platforms.5. Led all aspects of model review and validation, encompassing ongoing monitoring and governance in compliance with SR-11-07/OCC 2011-12 guidelines and the Enterprise Model Risk Management Policy.6. Owning key commercial and retail bank models to ensure proper model governing standards across the model lifecycle.- Data Science - GBM/XGBOOST, GLM, OLS, Logistic Regression Model- Quant Risk- Model Risk Management- Skills - SQL, Python, R, Big Data

Automotive Industry Consulting Group:CNG vehicles Market Entry decision for the client based on metrics:1. Price forecasting for Retail CNG based on Economic and Financial Modelling.2. Understanding national policy and government incentives to refine the modeling exercise.3. Identifying challenges from the perspective of vehicle adoption, safety, and upcoming substitutes.