Deepika Maniyil
Vice President, Quantitative Finance Manager, Ppnr Modelling @Bank of America
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WORK HISTORY
Vice President, Quantitative Finance Manager, Ppnr Modelling @Bank of America
Model Development: 1. Lead the development of Pre-Provision Net Revenue (PPNR) models to forecast granular balance sheet and revenue components, including Net Interest Income (NII) and Non-Interest Income under multiple hypothetical macroeconomic scenarios, across Global Markets, for CCAR, DFAST and ICAAP. Developed and enhanced forecasting models for Global Interest Rates and Global Mortgages and Securitized Products ( GMSP) lines of businesses, to forecast asset balances and P&L for linear and non-linear trading desks, leveraging flow-valuation decomposition, econometrics, time series analysis and robust statistical modeling techniques. Model Governance:1. Manage full model lifecycle including development, testing, documentation, implementation and performance testing.Stress Testing and Regulatory Engagement:1. Support enterprise wide stress testing exercises including CCAR, DFAST, and ICAAP submissions for Global Markets. Quantitative Analysis and Testing: 1. Conduct sensitivity analysis to assess model responsiveness to key economic variables and market drivers. Perform back testing, benchmarking and challenger analysis to ensure model accuracy and reliability. Programming and Technical Skills: 1. Develop and maintain production grade model code using Python and R.
EDUCATION
University of North Carolina at Charlotte
Master of Science (MS), Data Science
Anna University Chennai
Bachelor of Engineering (B.E.), Information Technology
SKILLS
ABOUT DEEPIKA MANIYIL
Quantitative Risk Modeler and Computer Science engineer with 8+ years experience designing and leading the development of statistical models by problem solving and promoting new methodologies, algorithms, tools and technologies. Experience working in a dynamic fast paced environment resolving critical issues using quantitative methods, time series analysis and Econometrics. Experience forecasting Net Interest Income ( NII), Non-Interest Income and balance sheet line items for Global Markets. Comfortable using Python and technology solutions in the implementation of models and data analytics.
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