Ramesh Laxmisan

Performed model reviews on firm models with focus on CCAR/CECL, GenAI, AI/ML models, and model implementation reviews. Adapted LLMs for model review work and spearheaded transformation initiatives within Model Risk Management.

Expanded Client Risk Analytics and P&L Explain to include Vega metrics. Analyzed model C++ code and addressed shortfalls in DV01 metrics, and helped build the Firm Funding Credit Curve in Python.

Advised on LIBOR Transition topics and quantitative modeling. Built SOFR and LIBOR CCAR/PPNR models for wholesale loan portfolios using Python.

Responsible for market risk monitoring across the wholesale book in AWM. Developed, tested, and implemented a new AWM HNW Mortgage model used in PPNR, CECL, budgeting, and stress testing.

Headed a cross-geography team responsible for creating, approving, and maintaining benchmark models for internal capital, budgeting, and CCAR. Led initiatives to automate benchmark models and conducted periodic reviews of PPNR and balance sheet.

Responsible for modeling discount curve used in liability valuation and conducting business line level ALM projects. Created a prototype Asset Liability Supervised Machine Learning Model in Python.

Implemented analytical framework and tools for factor-based planning and forecast for CCAR/Stress Testing. Created a Stress Test baseline model linking macroeconomic drivers to financial statement projections.

Created the first Asset Stress Testing model used in Fed CCAR and internal capital decisions. Applied quantitative and machine learning methods to support capital model development and risk quantification.

Performed detailed modeling and valuation analysis for complex and illiquid trading positions. Created pricing models for mortgage loan trading portfolios and developed methodology to value illiquid mezzanine debt.

Created the process for valuing the funding and FTP associated with each of Chase's mortgage portfolios. Findings used to make weekly and quarterly funding decisions.

Prepared a trade processing system and front end that monitored messaging, and performed portfolio analysis using Bloomberg/C++.

Created central database repository and reporting tools on different funds based on region, strategy, and product types.

Adapted a live trading model based on an equity pairs strategy for the UK and Japan Group, managing $100 million.

Internship in Private Wealth Management, Comparative Analysis.

AWS Certified Solution Architect certification in progress.

Master of Engineering in Operations Research and Information Engineering with a Dean's Certificate in Financial Engineering.

Bachelor of Technology in Metallurgical Engineering.

Machine Learning certification from Stanford University via Coursera.

Deep Learning Specialization, a five-course specialization from deeplearning.ai.