SOUJANYA POSANI

Address: *** **** *****, **********, ***** 75067 Phone: 469-***-****

Email: ad1g71@r.postjobfree.com

DATA ENGINEER

Q U A L I F I C A T I O N S P R O F I L E

Analytical, innovative, and results-driven professional with over 11 years of experience in data analytics and applications development; coupled with basic knowledge of data modeling and simulations and statistical techniques. Equipped with proven history of success in increasing portability of existing SAS programs and creating new programs using SAS macro variables to improve the efficiency and consistency of the results. Expert at implementing process improvements, processing and extracting value from large datasets, and creating data models in alignment with company goals. Highly capable of designing, planning, and executing various project simultaneously without compromising the results. Proactive team-oriented player, effective at building positive work relationships and collaborating with all individuals of diverse backgrounds. A R E A S O F E X P E R T I S E

Business Intelligence and Reporting Data Mining and Migration Data Validation and Cleaning Statistical Analysis Process Automation Complex Report Generation Staff Management Customer Service and Relations P R O F E S S I O N A L E X P E R I E N C E

TIAA-CREF, Charlotte, NC

Customer Insights Senior Analyst, Marketing Division 2015–Present

Develop model prediction to forecast customer behavior as well as retention models to prevent customers withdrawing from existing plans.

Complete the preparation of the exchange traded fund (ETF) data and formulation of codes in the process of model development according to the statisticians’ requirements.

Utilize and analyze email, direct mail, and web logins data for the campaign effectiveness.

Check model performance, consistency, and efficiency based on changing data over the years by validating most of the models.

Document all the models and serve as the go-to person for other teams in case of any knowledge transfers of the models and data involved in the Customer Insights Team.

Administer all the models in the Customer Insights Team, when the data sources transitioned to new ones, while ensuring their functions without any error during and after the change, which involve multiple test case scenarios, testing during the processes, and validation of the models after.

Render hands-on assistance in executing the following key functions:

− Standardization of the data and placement of the models and tables in production environments and on a scheduler;

− Conceptualization of new concepts used by the Marketing Team, such as customer segmentations based on the requirements of senior leadership;

Work collaboratively with the following individuals and teams to perform key initiatives:

− Multiple teams to migrate few reports and models into the production environment along with testing and continuous monitoring that ensure efficient transition of the process;

− Leads Generation Team to ensure that leads receive the best offers assigned based on the model prediction.

Make effective use of the following tools and methods to conduct assignments:

− Survey data to drive model generation and variable selection;

− Vintage portfolio analysis to provide the customer a lifetime value based on their assets;

− Multi-model amalgamation to project the best offers for each lead during the sales calls;

− Adobe Analytics tool to develop digital data transformations and model generations;

− Tableau to create monthly dashboards of the model validations as well as quick data analysis of new data sources;

− Hadoop to pull data from the sources and build new datasets in Impala and Hive;

− Snowflake in the extract, transform, and load (ETL) processes of the data;

− Speech analytics to analyze triggers in customer calls that may cause customer attrition. S o u j a n y a P o s a n i 2

Wells Fargo, Addison, TX

Analytics Consultant 2012–2013; 2013–2014

Developed a process flow automation to generate loans for the attorney audits based on the business rules obtained from the auditors, including data integration and transformation from different servers.

Created a model for the Sheriffs Fee Cost data from different sources and transmitted the result in a report format.

Addressed the reporting needs of the company in the Loss Control Vendor management system.

Integrated all the databases for the non-real estate departments into one common platform from where the loan data could be accessed.

Designed new applications, including statistical analyses of the attorney audit data, as well as an entire application in SAS, including the front end, data extraction, and ETL transformations

Demonstrated expertise in utilizing the following tools and methods:

− Looping concept in SSIS to handle multiple requests;

− SSRS and SSIS in Business Intelligence Development Studio (BIDS) to create and deploy reports in MS SQL Server environment;

− ETL (SSIS) to develop jobs for extracting, cleaning, transforming, and loading data into data warehouse;

− SSIS transformations, such as lookup, derived column, data conversion, aggregate, conditional split, SQL task, script task, and send mail task;

− SSRS to design different kinds of reports, such as planned, ad-hoc reports;

− Expressions in the variables to enable variables perform computations dynamically;

− Temp tables and data transfers to move the data between various servers and transformations to join or split them;

− MS Access to plan another project to prepare daily reports for the management;

− SAS/AF and SAS/FSP to program SAS GUI applications;

− SAS- Output Delivery System (SAS/ODS) to format file in HTML, RTF, and PDF;

− MS Excel to construct multiple tools for the auditors. Nation star Mortgage, Lewisville, TX

SAS/ Root Cause Analyst 2013

Maintained daily communication with various departments to gather and analyze data to facilitate a continuous study of patterns and trending of the loans in various portfolios.

Consolidated the data from various departments of the servicing portfolio to create monthly performance review reports for the Risk Management Team.

Determined the concentration of bad or risky loans in various states through designing of the heat charts in SAS.

Streamlined all the data sources from various databases and performed transformations to obtain the final outcome and to drive automation in SAS and SQL for the generation of reports.

Identified data discrepancies through root cause analysis.

Obtained trends and comprehensive direction on each of the servicing portfolios through slicing and dicing of data. Walgreens, Chicago, IL

SAS/Business Data Analyst 2010–2012

Effectively directed two teams with respect to the operations and delivery.

Analyzed the effectiveness of the ongoing programs in the logistics and merchandising areas through ANOVA, multiple regression, and correlation studies.

Established new tools for inventory management and efficient forecasts.

Executed the clustering of stores that were identified by key demographics and stores, when re-planned would result in significant yield.

Took charge of completing data validation and cleaning, administering the coding part of simulation applications, and extracting data from several databases, including Oracle and flat files.

Used SAS Dbload procedure to upload SAS data files into ORACLE tables.

Drove strategic efforts in developing the following:

− New tools for inventory management and efficient forecasts;

− Permanent formatted SAS data sets and reports using PROC REPORT, PROC TABULATE, and DATA_NULL for analysis;

− Flat files for third-party vendors

Exemplified proficiency in using the following tools to perform technical initiatives:

− SAS SQL to convert Oracle data tables into SAS data files;

− SAS Dbload procedure to uploaded SAS data files into Oracle tables; S o u j a n y a P o s a n i 3

− SAS Tools- SAS/Base, SAS/Macros, SAS/Graph, and SAS/Access to produce SAS data analysis and statistical analysis-generated reports and graphs;

− SAS- Output Delivery System (SAS/ODS) to format HTML, RTF, and PDF reports;

− chi-square statistic and correlation coefficients with SAS to analyze the significance of store attribute and demographic profiles and performance

− SAS for pre-processing data, SQL queries, data analysis, reports generation, and statistical analyses. E A R L I E R C A R E E R

Mu Sigma, Bangalore, KA, India

SAS/Data Analyst

TCS, City, State, India

SAS Analyst

E D U C A T I O N A N D C R E D E N T I A L S

Master of Technology in Industrial Engineering

Virginia Polytechnic Institute and State University Blacksburg, VA, USA Bachelor of Technology in Chemical Engineering

Birla Institute of Technology & Science, Pilani Pilani, RJ, India Certified SAS Professional – Granting Institution

T E C H N I C A L S K I L L S

SAS Tools: SAS 9.1.3/9/8.2, Base SAS, SAS/ACCESS, SAS/STAT, SAS/Graphs, SAS Macros, SAS/SQL, SAS/CONNECT, and SAS/ODS

Statistical Tools: SAS and R

RDBMS: Oracle 10g/9i, Hadoop,SQL Server, and MS Access Other Tools: TOAD, WinSQL, Teradata SQL Assistant, CuteFTP, Putty, SSIS, and SSRS Operating Systems: Windows 95/98/2000/XPNista and UNIX Languages: SQL, C, C++, HTML, and XML

Office Tools: MS Office, MS Word, MS Project, and MS PowerPoint

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