Data Analyst
Resume:
Nagarjuna Kukkapalli
*** * **** ***, *** *, Bowling green, OH, 43402
************@*****.***
An analytics professional with over 3 years of experience in analyzing and supporting business operations with strong background in statistics and machine learning. Currently looking for a full time position to utilize my strong skills and analytical ability to achieve the goals of your organization.
SKILLS
Machine Learning: Gradient Boosting, Random forest, Neural networks, Decision trees, Ensemble techniques.
Big Data: Hadoop (HDFS), MapReduce, Hive.
Data Visualization & other tools: Power BI, Tableau, SAS E miner, R studio, SPSS.
Programming skills: SAS, SQL, R, Excel VBA.
MS Office: Word, Excel, Outlook, PowerPoint, Excel Pivot tables, Excel Analytic solver, VLookup.
Expert knowledge on SQL and relational database management system.
PROFESSIONAL EXPERIENCE
Genpact India Pvt. ltd Hyderabad, India
Senior Associate Oct 2012 – Jun 2015
Created and automated reports to save client an average of $2000 per year.
Automated day to day activities using various customized scripts written in SAS and SQL for various customer requirements.
Developed SAS code and SQL code to extract the data from oracle and SQL Server and create datasets, reports and presenting the necessary information in varied file formats.
Created Ad-hoc data analysis using SAS and SQL to identify statistical abnormalities and problem identification.
Provided solutions to repetitive tasks using SAS and SQL scripts according to client demands.
ReachOut Business Analytics Hyderabad, India
Intern Data Science July 2014- Nov 2014
Worked on the analysis to determine the football team with the highest chances of winning the state championship using R and SPSS.
Analyzed the role of middlemen in marketing products and their impact on socio-economic conditions of fishermen.
Nuevora Analytics Pvt. Ltd. Hyderabad, India
Analyst Dec 2011- Sep 2012
Build end to end models using SAS and R tools.
Data Cleaning: is done using both heuristics and statistical based techniques.
Coding: Coding/binning is done using SAS Macros, and SAS/SQL.
Preliminary Statistical Analysis: Generating tables using PROC FREQ, PROC MEANS, and PROC TABULATE to have quick understanding of data dynamics
Variable Selection: Using techniques like WOE (Weight of Evidence) and VIF (Variance Inflation Factor)
Model Building: Applying PROC LOGISTIC to build the model using Normal Logistic Regression, Backward Stepwise Regression, and Forward Stepwise Regression
Reporting: using PROC REPORT and SAS/GRAPH developing the key outputs and graphs.
Involved in Delivering output using SAS/ODS (Output Delivery system)
PROJECTS
Customer Churn (P&C Insurance)
Description: The churn model is one such model that allows an insurer to retain high value customers by allowing them to predict the churn probability of individual customers. The investors can then develop retention strategies based on the output of the Churn Model.
Techniques used: Logistic Regression.
Claims Fraud (P&C Insurance)
Description: We are concentrating on the P & C Claims Fraud data and also we are analyzing the Factors for occurring the Particular Claim to be fraudulent or non-fraudulent made by the Customers. We also found out the relationship between the variables as well as the KPI’s.
Techniques used: Negative Binomial Regression, Logistic Regression.
EDUCATION
Master of Science in Analytics Expected: August 2016
Bowling Green State University, Bowling Green, OH.
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