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Model Implementation/data Analyst Resume

5.00 Rating

Mclean, VA

SUMMARY:

  • Strong quantitative, programming and analytical skills. Proficient in Statistical analysis, Predictive Modeling, Credit Risk Analysis and statistical programming.
  • Excellent written and verbal communications skills. Team facilitator; willingness to learn.
  • Risk Management, Credit Card Fraud, Basel - 2, Database Marketing, Segmentation & Clustering, Linear and Logistic Regression, Time Series Analysis, Multivariate Analysis-Principal Component, Factor Analysis Discriminant Analysis, SAS/SPSS/PASW/STATA, SAS Enterprise Guide, R, SQL, Teradata, VBA, KXEN, Data Mining, Test/Control Design, Survey, Data Preparation, Model Building and Validation, Application Development in UNIX and Shell Scripting, Scenario Analysis and Stress Testing

COMPUTER SKILLS:

Statistical Software: SAS 9.1 - DATA Step, BASE SAS, PROC SQL, SAS/STAT, SAS IML, SAS ETS, SAS Macro Programming, SAS Enterprise Miner, SPSS, PASW, STATA, R, Win-bugs, KXEN, CHAID

Applications: Word, PowerPoint, Excel- Pivot Tables VLOOKUP Index VBA, MATLAB (Numerical Analysis), UNIX Scripting Optimization- Excel Solver, AMPL Simulation- Arena 10.0

Languages: C, C++, SQL.

Database: Oracle, MS Access, MS SQL Server 2005, PostGRESQL, SAS/SQL

Mainframe: MVS, JCL, COBOL, CICS, DB2

WORK EXPERIENCE:

Model Implementation/Data Analyst

Confidential, McLean, VA

Responsibilities:

  • Writing, maintaining and implementing credit models in SAS for Mortgage and Auto Loans
  • BASEL 2 PD, LGD, EAD segmentation, RWA calculation and model implementation for Mortgage portfolio
  • Loan Loss Forecasting for Mortgage Portfolio; calculating accounting and economic losses
  • Model Validation using SAS/STAT; model documentation and writing white papers
  • Coding in SAS Enterprise Guide and SAS Macros for model implementation

SAS Systems Developer

Confidential, Columbus, OH

Responsibilities:

  • Develop SAS Macros for Anti Money Laundering services; Analytics using SAS, SAS Macros, SAS SQL
  • Modifying existing scenarios and creating new scenarios for AML/BSA

Lead Analyst

Confidential, Hoffman Estates, IL

Responsibilities:

  • Develop propensity models for targeting customers.
  • Writing queries in Teradata for complex ad-hoc business analysis

Confidential, Downers Grove, IL

SAS Consultant

Responsibilities:

  • PD Modeling by regression methodology using mortgage loan specific characteristics like LTV, FICO and macroeconomic variables
  • Using multivariate statistical methods, data mining and econometric methods for segmentation, credit risk modeling, regression analysis, forecasting and Stress Testing
  • SAS ODS reporting and generating monthly reports (Asset Liability Management, Performance Analysis)

Confidential, Naperville IL

Sr Consultant

Responsibilities:

  • Automating retail and contract pricing using SAS. Data Mining and Data Preparation. Report automation using Excel and SAS

Confidential, Phoenix, AZ

Manager (Risk Management)

Responsibilities:

  • Credit card fraud mitigation strategies to enhance profitability.
  • Generating reports in SAS Enterprise Guide. Creating views and tables using PROC SQL and Teradata
  • Modified statistical models to calculate ProbF and Lift- probability measures to calculate fraud risk of a transaction

Confidential, Schaumburg, IL

Sr. Statistical Analyst (Marketing Analytics)

Responsibilities:

  • Coupon redemption by Smartphone customer analysis with retail data using SAS, Logistic Regression, Optimization- Linear and Integer, Kernel Density Estimation. Correlation/Covariance Analysis
  • Reporting ad hoc SQL query metrics using Teradata to client.

Confidential, Mettawa, IL

Sr. Credit Policy/ Risk Analyst

Responsibilities:

  • Estimation and validation of risk parameters - Probability of Default (PD), Exposure At Default (EAD) and Loss Given Default (LGD) for Basel II regulatory compliance.
  • Created and maintained econometric models that forecasted the levels, percentages, and/or characteristics of commercial loans for risk management and Stress Testing purposes.
  • E Developed clustering model using K-means with internal and bureau variables to identify risky pockets of population as an alternative to segmentation modeling using SAS, R

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