Summary:

• SAS certified in Predictive modelling and BASE SAS programming
• SAS Professionally trained in SAS business intelligence, SAS business analytics and SAS advance programming
• Professional experience of 3+ years of working on different statistical tools, software and techniques
• Implemented projects with BASE/SAS, SAS MACROS, STAT, PL/SQL, GRAPH, OLAP, PROC, DATA, FORMAT,

ODS in windows environment involving ETL processes

• Extensively involved in the creation of SAS data sets, Reports, Listings, Graphs, tables, Adhoc reports and dealing with large datasets according to the Standard Operating Procedures (SOP’S),reviewing CRF’s
• Specialization in SAS programming, Data mining and Database Marketing area
• Extensive experience in using Business intelligence (BI) applications like SAS OLAP Cube studio, SAS Web Report Studio, information map studio, SAS add-in for MS office and SAS Enterprise guide (EG) for creating, editing & managing reports in live projects
• Strong analytical, modelling, communication & presentation skills
• Strong knowledge involving all phases (I-IV) of clinical trials
• Expertise in analyzing and coordinating clinical trial data, generating reports, tables, listings and graphs.
• Extensive Experience in developing reports for Clinical Trials & Health Insurance
• Hands on experience in applying different statistical methods and advanced business analytical tools (BA) in professional environment using SAS enterprise miner (EM), base SAS, SAS enterprise guide
• Experience in software such as VB 6.0, C, C++, Excel, SQL server 2005

Skills:

SAS SAS/BASE, SAS/STAT, SAS/GRAPH, SAS/MACROS, SAS/OLAP, SAS/ACCESS, SAS/ODS, SAS/CONNECT, SAS/IML, SAS/DI SAS Enterprise Miner, PROC Means/Univariate/Import/Export/Logistic/Transpose

BI Tools OLAP cubes, SAS Web Report Studio, SAS Add-in for MS office, SAS Enterprise Guide

Language Base SAS, VBA, C, C++, SQL

Advance SAS Tools Clustering and segmentation, self organizing maps(SOM) Kohonen, Support Vector Machines (SVMs), Two stage models, component models, customer attrition and churn models via survival analysis, credit scoring models,  Customer Lifetime Value (LTV) Analysis, Text Mining

General Tools: Regression (Linear, Logistic), Tree, Neural, GLM, Gradient Boosting, Memory Based Reasoning (MBR), RFM Analysis, Time Series Analysis, t-test, ANOVA, ANCOVA, bootstrapping (Bagging)

Affiliations:

• ‘Alpha Pi Mu’ (Industrial Engineering National Honour Society)
• Association for operations management ( APICS )

Education:

Master of Science in Industrial Engineering & Management,
Specialization: Statistical analysis and data mining 

Courses taken: Database Marketing, Data Mining & CRM applications, Advance Data Mining Applications, Data Process & Object Modelling, Creating customer value using digital marketing, enterprise resource planning, Statistical experiments, Breakthrough quality Using Statistics, Advance finance and investment analysis, service systems and business processes, Strategic quality leadership through statistical analysis 

Certifications: 
Predictive Modeler using SAS EM 5 
Base SAS programmer using SAS 9 
GraduateCertificate in Business Data Mining 
Advance Base SAS Programming 

Work Experience:

Client: Confidential Jan 2008-July 2009
Statistical Modeller/Analyst/SAS Programmer

Project Description: 
Confidential works to provide different health services to state counties such as child and family health care. This project is to address their problem of non renewal of patient health plans. Patients which are not renewing their health plan on right time (free of cost) causes OSDH loss of revenue because they do not get that money from state authority due to lack of these patients coverage at that time. OSDH wants to know the trend of those people who are not renewing at right time and reason behind it. Our job was to determine causes by analysing their existing database of patient records.
Responsibilities: 

• Worked in area data mining to provide them with a suitable solution to address non-renewal problem for their family planning division which was causing them huge loss of revenue
• Phase I:Business issue, analyzing variable distributions, Data Cleanup, Inconsistencies, Outliers, Conversion to meaningful categorical variables, Analysis of independent variables with dependent variable (Base SAS,SAS OLAP cubes, SAS EG, ETL)
• Phase II:Created combination variables, Changed the values of the variables from descriptive categories to numerical categories, analyzed error rate, ran different predictive models on test sample, Identified the best performing predictive model, Identified the equation, Translated into meaningful business rules and Success rate of the model (Base SAS, SAS/graph, SAS/ODS, SAS/Macros, Proc Sql/means/logistic, SAS EM &SAS EG)
• Phase III: Creating and modifying reports, Optimize each of the main variables, Scenario analysis for each of the Key variables, Excel template for changing the cost values, Scenario analysis for achieving different eligibility or denial rates, Cost impact, Margin impact, Net profit and loss effect (SAS/Graph, SAS/ODS, SAS/macro, Proc Plot, BI tools – SAS add in, web report studio, Excel)
• Models: Logistic regression, neural network, decisiontree, gradient boosting, Memory based reasoning (MBR)
• Outcome: Developed a model to identify people with possible renewal problem and to address other issues Like modifying 270/271 submission process, decreasing error rates. This resulted in annual savings of about $0.5 M for the client.

Environment: Base SAS, SAS OLAP cubes, Enterprise Guide, Enterprise Miner, ETL, SAS/ODS, SAS/SQL, SAS/
Macro, SAS/STAT, SAS/Graph, Business Intelligence tools – SAS add in, web report studio, SAS Information studio, Excel Macros in VBA, SQL server

Client: Confidential, USA Jan 2009-June2009
SAS Programmer/Analyst

Project Description:

Confidential is a provider of plastics, chemicals and agricultural products and it is the second largest Chemical manufacturer in the world by revenue. We as a part of the development team need to create models which could predict the yield of wheat, corn, soybean and Energy Grass per county depending on the soil and weather conditions of that county. Using the density function we are to determine the top three densest counties and states so that American Energy Resources could build processing facilities for ethanol production from Energy Grass.

Responsibilities:

• Completed data mining project for deciding best locations(Segmentation) within US for ethanol manufacturing plant from energy grass based on historical data available(Soil, Crop, Weather) in 2400 counties
• Analysis: Data preparation, analyse variables, create variables, development and comparison of multiple segmentation models, profit calculations and optimization of selected model, creating and modifying reports (SAS EM, Base SAS SAS/Macors, SAS/ODS, Proc Means/Logistics/Import/Export/graph, SAS BI tools – SAS add in, web report studio)
• Outcome: Suggested top counties and states where energy grass production is profitable option. Helped client in huge possible savings in terms of yield per acre of about 40-50% as well as increased profit for the farmers

Environment: SAS 8.2/9.1.3, VB, EXCEL, Base SAS, SAS OLAP cubes, Enterprise Guide, Enterprise Miner, ETL, SAS/ODS, SAS/SQL,SAS/Macro, SAS/STAT,BI tools, SAS add in, web report studio, SAS Information studio, Excel Macros in VBA

Company: ConfidentialOct 2005 – April 2007 
SAS Programmer

Description:

Confidential is a global IT Solutions Company specializing in IT Services, Survik offers flexible and collaborative solutions to meet the specific needs of their clients in Healthcare, Utilities, Federal Government, IT & Software, Education, Manufacturing, Banking, and other industries. It offers software consulting and development services on various platforms including SAS.

Client: eTrials USA (Oct 2005-July 2006) 
SASProgrammer: Clinical Trials

Responsibilities:

• Reviewed clinical study protocols and CRFs. Worked with biostatisticians and clinical staff to generate data summarization, analysis and reports.
• Used the SAS ODS facility to write custom safety and efficacy reports & directing SAS output to RTF files.
• Developed Tables, Graphs and listings for Clinical Study Reports using SAS/Graph, SAS/ODS, SAS/Macro, SAS/STAT, Proc SQL/freq/means/plot
• Produced quality customized reports by using PROC REPORT and also provided descriptive statistics using various procedures like Proc Univariate, MEANS, FREQ.
• Data cleaning by edit checks in compliance with the SAP
• Prepare ADHOC Tables and Listings
• Validation and program documentation for Datasets and TLF’s
• Developed programs by efficiently using Macros, SQL joins, ODS and various statistical procedures.

Environment: SAS 8.2, SAS Business Intelligence tools, SAS web report studio and SAS MS add in, Base SAS, SPSS, Enterprise Miner, Minitab, SAS/SQL, SAS/Macro, SAS/STAT, SQL, MS Access, Excel

Client: Confidential (June 2006-April 2007) 
SAS Programmer/Analyst - Clinical Trials

Project:A Study to Evaluate the Efficacy and Safety of Herceptin (Trastuzumab) in Combination With an Aromatase Inhibitor in Patients With Metastatic Breast Cancer.

Study Type: Interventional

Study Design: Treatment, Randomized, Open Label, Parallel assignment, safety/efficacy study

Responsibilities:

• Maintained appropriate application reference documentation.
• Provided SAS programming and statistical support to Clinical studies
• Created and maintained SAS Datasets that are extracted from an Oracle Database.
• Using SAS/STAT to response-surface regression modeling to examine how acidity and mixing time affect the yield of new chemicals.
• Perform an exact test to evaluate the success of a new pain medication on headache relief in a small study using SAS/STAT.
• Generated graphs using SAS/GRAPH and SAS Graphics Editor.
• Developed routine SAS macros to create tables, graphs and listings for inclusion in Clinical study reports and regulatory submissions and maintained existing ones.
• Developed SAS macros for data cleaning and Reporting and to support routing processing.
• Created SAS Macros and SAS Graphs. Used Proc REPORT to generate reports
• Convert MS-Word documents and Excel tables into SAS data sets.
• Read data from Oracle, Excel and flat files into SAS software. Developed SAS code to clean the invalid data from the database. Creating Summary Reports and Tabular Reports using Proc Report.
• Used SAS/STAT software for Statistical Analysis, procedures and reports.

Environment: SAS/BASE, SAS/MACRO, SAS/ACCESS, SAS/CONNECT, SAS/GRAPH, SAS/STAT, Oracle 8, Clinical Trial.