SUMMARY:

• 10 years’ experience as Researcher/Analyst on building quantitative economic and financial models, implementing economic and statistical analyses and making predictions using SPSS, Eviews, R, SAS, Matlab, etc.
• 7 years’ experience in SAS Data Processing and Data Management.
• Extensive knowledge of advanced econometrics and statistics skills and tools such as SQL, C++, SAS, R, Excel, Matlab and ST. Certification of Office Proficient User and Office Expert.
• Strong background in predictive modeling using SAS modules (SAS/BASE, SAS/STAT, SAS/MACROS, SAS/ACCESS, and SAS/SQL) for Linear, Nonlinear, Logistic, GLM, Mixed, Time Series etc.
• Experience with PROC SQL, PROC DATASETS, PROC FORMAT, PROC PRINT, PROC APPEND, PROC TABULATE, PROC COMPARE, PROC IMPORT, PROC EXPORT.
• Generated reports using PROC REPORT, DATA NULL and PROC TABULATE for customized report writing.
• Experienced in producing RTF, PDF, HTML files using SAS ODS facility.
• Extensively worked on various RDBMS like Oracle and SQL Server.
• Experience in carrying out Marketing Campaigns and evaluating customer information.
• Proven skills in data cleansing, data archival, data migration, ad - hoc reporting, and coding using SAS on various environments.
• Solid SAS skilled in statistical analyses using statistical methods.
• Rich experience in SAS Marketing Research and Business Analytics through data mining, optimization, and decision tree etc.
• Proficiency in SAS programming and R programming in Statistical analysis, predictive modeling, data mining, and optimization.
• Fast learner and solid analytical, problem-solving skills. Reliable, passionate and well-independent worker and good team player. Excellent in oral and writing communication skills in English.
• 7 years’ experience on statistical analysis and predictive modeling using SAS.
• Strongly Skilled in the following SAS Tools: SAS/Base, SAS/Access, SAS/SQL, SAS/Macros, SAS/Graph, SAS/Stat, SAS/ODS, SAS.ETS, SAS/Report.
• Proficiency in predictive modeling using SAS to fit Linear Statistical Regression Models, Logistic Regression Models, Mixed Models, Time Series Analysis and Modeling, do the Regression Diagnostics and check the Goodness of Fit.
• Excellent in using SAS to analyze Qualitative/ Quantitative data set, to do Statistical analysis, Statistical tests, ANOVA, GLM, Regression Analysis, Bayesian data analysis, Univariate/Multivariate Data analysis, Time Series Analysis, Categorical Data Analysis, Survival Analysis.
• Importing various external data files into SAS Dataset, SAS/library by SAS/INFILE, SAS/IMPORT, SAS/SQL, SAS/ ACCESS.
• Importing/exporting data files using SAS/ACCESS SQL Pass-Through Facility, LIBNAME, PROC IMPORT/EXPORT, PROC COPY, PROC CPORT, and Data Step programming.
• Exporting SAS results to different formats, such as HTML, Excel, and PDF by using SAS/ODS for report and presentation.
• Prepare data set, manage, merge, clean or reformat dirty raw data set, transfer data set from one platform to another; map data between SAS and XML or other data formats using SAS or R tools.

COMPUTER (SOFTWARE) SKILLS:

Other Software: SQL, C++, R, SAS, Matlab, STATA, SPSS

Operating Systems Platforms: Windows 2000/NT, Windows XP/Vista

Database & Data Warehouse: Oracle, SQL Server, Access

Office Tools: Expert in Microsoft Office (Word, Excel, PowerPoint), MS Outlook, MS Project, Adobe Acrobat

PROFESSIONAL EXPERIENCE:

Confidential, Princeton, NJ

SAS Data Analyst

Responsibilities:

• Extracted relevant data from the warehouse using SAS/ACCESS LIBNAME facility and SQL Procedure Pass-Through facility.
• Used stratified separate sampling techniques for predictive modeling sample.
• Transformed transaction and event data by tabulation summaries, distribution characterizations, stratified analyses, profiling and time series methods.
• Created transaction macros: sum, std, max.
• Merged transaction inputs with target sample.
• Recoded non-numeric inputs by Enumeration, Dummy coding and Target-based transform.
• Managed data pathologies by detecting and taming extremes and managing missing values.
• Developed SAS procedures, Macros and applications for Data Quality Checking (DQ) and data cleaning: proc contents, PROC MEANS, PROC FREQ, ods select, PROC UNIVARIATE, PROC FORMAT, PROC SORT, PROC SQL and many SAS data steps such as ARRAY, MERGE, SET, etc.
• Created a one-row-per-subject SAS dataset for Customer Segmentation.
• Applied Campaign Matrix to derive Product/Offer for each customer. Created a one-row-per-subject file (or Data Mart) that can be used in building various types of predictive models.
• Split the file into sample files based on given percentages: PROC SURVEYSELECT.
• Created a file with calculated roll-rate (RRs) for each delinquency bucket (DO loop in data steps).
• Created a one-row-per-subject SAS dataset where all RR rates are 3- month-moving-average (PROC SORT, a macro to create 3-month moving average and other SAS data steps such as SET, ARRAY, RETAIN and RENAME).
• Expanded the file to include Event Indicator (Charge Off) with value of 1 or 0. Merged this file with final file from Project 2 (PROC SQL).
• Calculated ‘Time To Event’ in terms of Months Till Charge Off for each account.

Confidential, Princeton, NJ

SAS Programmer

Responsibilities:

• Wrote code using SAS/BASE and SAS/MACRO to extract data from Excel file, Access and Oracle Database, CSV.
• Integrated data from different studies for statistical analysis and reporting.
• Performed data analysis, statistical analysis, generated reports, listings and graphs using SAS/BASE, SAS/MACRO and SAS/GRAPH, SAS/SQL, SAS/STAT.
• Developed SAS/BASE, SAS/MACRO, SAS/GRAPH to create tables, graphs & listings.
• Create SAS output report to RTF, PDF and HTML format using ODS.
• Produced customized reports by using PROC TABULATE, PROC REPORT, and PROC SUMMARY and also provided descriptive statistics using PROC MEANS/MEDIAN, PROC FREQ, and PROC UNIVARIATE.
• Provided simple/multiple linear regression model using PORC REG, PROC GPLOT, PROC CORR, PROC TTEST, PROC GLM, PROC PLOT, PROC RANK; Marketing Research using CLUSTER, FACTOR, LOGISTIC, GLM, etc.
• Descriptive Statistical Analysis such as median, mean, mode, Standard Deviation, Histogram and Box Plot.
• Simple/Multiple Linear Regression Analysis and Modeling using T-TEST, F-TEST, p-value, CI, Correlation Coefficient, R-square, Estimate Parameters, Scatterplot, AIC, BIC, Backward, Forward, Stepwise Methods.
• Marketing Research using Multivariate Analysis, Logistic Regression Analysis, GLM, Predictive Modeling, Cluster Analysis, Factor Analysis, Sequence, predictive modeling, Optimization and Data Mining.

Environment: SAS 9 (Windows XP/ UNIX); SAS BASE, MACRO, ACCESS, GRAPH, STAT, SQL, ODS; MS SQL, MS Access, MS Excel.

Confidential

Statistical Data Analyst

Responsibilities:

• Worked on advanced statistical methods of analysis and software packages like Base SAS, SAS/STAT and R in helping and solving different problems and ad-hoc requests.
• Worked with massive databases (hundreds of terabytes) validating them with different statistical tests and also experience in marketing related modeling applications, database marketing, and predictive analytics.
• Automated the process of reporting which indicated metrics on profitability of different customer groups, profitability of different using SAS/BASE, SAS/MACRO, SAS/STAT, and SAS/GRAPH.
• Worked on Predictive modeling and managing the models for better view and reporting to ensure the quality of the product designed.
• Assisted in investigating and applying new SAS programming methods and techniques to enhance current systems by using various SAS macros and standard programming procedures.
• Performed various cluster analysis, using ANOVA, CHAID.
• Performed extraction, transformation and loading from large Oracle tables.
• Extensively used procedures like PROC IMPORT, PROC FREQ, PROC MEANS, PROC SORT, PROC PRINT, PROC TABULATE, and PROC REPORT.

Confidential

Financial Data Analyst

Responsibilities:

• Develop and maintain SAS programs to test the market overreaction hypothesis and the capital pricing model using PROC MEANS and SAS MACRO.
• Developed a SAS program to create a report on daily and historical quantities and profit and loss of the different generation.
• Designed and creating SAS datasets from various sources like Excel datasheets, flat files and Oracle, creating reports and files from existing SAS datasets.
• Performed trend and forecast analysis and reporting with various SAS products SAS/BASE, SAS/SQL, SAS ETS, SAS/ACCESS and SAS/MACROS, etc.
• Involved in market segmentation and customer profiling.

Confidential

Data Scientist

Responsibilities:

• Worked with huge datasets to extract customized reports using PROC SQL, PROC SORT, PROC REPORT, and PROC TABULATE.
• Created SAS datasets from Oracle database with random sampling technique and created Oracle tables from SAS datasets by using SAS Macros.
• Created large datasets by combining individual datasets using various inner and outer joins in SAS/SQL and dataset merging techniques of SAS/BASE.
• Analyzed data using various statistical procedures like PROC SUMMARY, PROC MEANS, PROC FREQ, PROC UNIVARIATE, PROC REG and PROC ANOVA.
• Used SAS/Macro facility to create macros for statistical analysis, reporting results and data extraction.
• Constructed a possible model: nonlinear LS for calculating premiums based on age after checking the scatterplot, R was used for construction.
• Transformed the nonlinear LS model to a linear model to get initial values for the coefficients for the nonlinear LS t.
• Estimated the variance for the residual and the 95% con dence interval for both the residual and the coefficients.
• Used bootstrapping to get 90% con dence limits for the correlation between the coefficients.
• Calculated log(Premium) depending on Age with full interaction terms between gender and smoking habits.
• Worked as a researcher on a project of devising a new management system of fisheries for Confidential, analyzed the biomass and escapement data of different species of fisheries of Confidential .
• Estimated the growth model of the different species and investigated a new fisheries management considering stock growth uncertainty and capital adjustment.
• Analyzed the relativity between the growth rate of PPI (GPPI) and the growth rate of momentumized money (GMV) empirically: used ADF test to check the existence of unit roots of the relevant economic variables; used EG two-step method and AEG test to check the existence of co-integration between GPPI and GMV series; built an error correction model of GPPI and GMV series; finally, Granger causality test further validated the importance of momentumized money in predicting macro-economic variables.
• Set up a model of the demand for momentumized money: the relationship between the demand of momentumized money (MTMR) and two macro variables: GDPR (GDP growth rate) and IR(growth rate of interest rate): used ADF test to check the stationarity of the relevant time series, set up an ADL model (auto-regressive distributed lag model or ADLM) to delete the serious auto regression among the error terms, used Stepwise method in SPSS to filter significant covariates and lagged variables, finally, compare the regression of the demand of momentumized money to the traditional money demand function.
• Analyzed the human resource strategies of the multinational firms and provided advices for Chinese enterprises to form human-centered management strategies.