Quant Model Validator Resume

SUMMARY

15 years experience in analyzing and designing data requirement, conversion business requirement into functional requirement for Credit Risk stress testing methodlogy for CCAR reporting. It covers mathematical validation used in credit risk forecasting and stress testing.
8 years experience in hands on experience in developing SAS statiscal moethods of Regression models, and models with classification effects, univariate and multivariate modls, fixed, random and mixed models, generalize linear moels, latent variable models along with Bayesian models.
Developed multhireaded R framework to run 1000s of R code to run asynchronoously. Using a queue that runs code as subprocess. It has pop and push mehtods to deliver and detach code. Develped performance tuning in R code to run effeicient running of codeusing R tidyverse
Having hands on experience in computing different VaR models, improve risk reports for new asset classes and providing analytic support to front office for equity, fixed income and credit derivative products.
Having hands on experience in interact with service FDIC and implement assumptions and calibrate models to current market price and adjust assumptions and parameters of theoretical models.
8 year experience in Quant Model analysis and implementation and validation of VaR and CVA risk models and involving in quant research activities. It includes CDS, CDO calculation for credit derivatives.

TECHNICAL SKILLS

Languages: R/SAS/SQL/VBA and Python, R tidyverse

RDBMS: Sybase, Oracle

Functional Domain: Credit VaR computation for Credit models for commerical loan

PROFESSIONAL EXPERIENCE

Confidential

Quant Model Validator

Responsibilities:

Analyse and generate high qualirty reports about credit risk and level of exposure and credit requirement for different portfolio and product level.
Mapping pricing and sensitivity models, data validation and identify key concentration of risks in portfolio ..
Generate reports for various Fed specific macroeconomic variables and run stress and sceanrio testing and compute CCAR reports.
Collect metadata of previous release data and analysing about topology and parallel run of valuation method...
Assist with model validation across all asset classes (liaison to model reviewer) .
Communicate with stakeholder, team and project management.

Environment: SAS, VBA, Python, R tidyverse

Confidential

Lead VAR Model Analyst/Developer

Responsibilities:

Perform analyses of credit risk in portfolio segments providing insight into historical risk trends, projected levels of risk and performance, and pricing for risk using various methods including predictive models and standard industry analysis.
Assist in risk management projects to help understand segmentation of the Commercial Portfolio.
Provide input and support to ensure data quality in for Information Delivery group projects. Reporting Prepare, review, and distribute credit risk reports to executive management and board level committees.
In incremental risk charge, implemented to capture default and credit migration risks along with risks by VaR for credit sensitive positions
Applied market risk standardized approach to compute regulator capital for securitizations and nth to default credit derivatives

Environment: SAS. Analytical libraries in C++, VBA, Python

Confidential

Quantitative Analyst/Developer

Responsibilities:

Configuration and customization of forward Curves. It includes position valuations, currencies, interest rates and calculate volatilities and correlation
Valuation & Disaggregation It includes configure portfolio valuations. configure valuation models and valuation segments valuation segments for optimal performance, Utilize multiple valuation models for reporting
Exposure development. t involves configure, review and evaluate position exposure, Configure position reports for view exposure
Simulation implementation. It involves development of scripts to perform “stress” portfolio by simulating adjustments (up and down) to various parameters, impacting position and market value, generate “on - the-fly” simulations, configure and reuse simulations.

Environment: Unix, SAS, C++, sybase