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Location: Richardson, TX, USA
University: The University of Texas at Dallas
Major: Business Analytics


Address: 800 W Renner Rd, #3928, Richardson, TX - 75080 | Cell: xxx-xxx-xxxx | |DELL EMCCertified Data Science Associate| TableauCertified Desktop Specialist
Southern Enterprises, Coppell, TX Data Analyst InternJune 2018 – Present
● Developed 90% accurate forecasting models in R, Python using time series analysismethods for furniture SKUs
● Queried MS SQL Server and MySQL databases and developed Power BI reports and dashboards for Supply Chain team
● Automated daily tasks and streamlined business processes using Excel VBA Macros and Power BI
e-GMAT, New Delhi, India Operations Manager Aug 2014 – Sep 2016
● Developed dashboards and developed recommendations resulting in 80% increase in leads, revenues, and customers
● Analyzed customer data improving processes and offerings, reducing customer queries by 35%
● Designed algorithm for a scoring tool that helps students; used regression models to debunk scoring myths
MapmyIndia, New Delhi, India Engineer – Sales & Marketing June 2013 – July 2014
● Developed GIS-Integrated Business Intelligence Solutions & Telematics Tracking Solutions to solve business problems
● Executed data-driven growth hacks to achieve targets for product usage and community size
1) Multiclass Classification of Cardiac Arrhythmia ECG Data
o Objective: Distinguish between the presence and absence of cardiac arrhythmia and classify it in 1 of the 16 groups
o Given: 279 attributes and 452 instances from a medical database
o [Python]: Came up with an evaluation strategy to classify the data and usedgrid search on various classification techniques including KNN, logistic, linear & kernelized SVM, Decision tree, & Random forest to find best parameters
2) Determination of Short Tail Keywords for Marketing
o Objective: To determine list of short tail keywords for social media ads and SEO
o Given: Title, Author, Groups, Keywords, Topics and abstract for 400 research papers
o [Python]: Converted textual data into numeric form suitable to apply clustering and generated more features from existing main features. Applied k-means clustering. Made word clouds to generate an intuitive sense of the clusters.
3) Analysis of Intern data to improve job acceptance rates for a Dallas based Global Technology company
o Objective: Analyze factors behind decision of interns to accept or reject full time offers
o Given: Responses to 30 questions (categorical (0-5) and textual) of total 1000 participates in 3 different surveys
o [R, Tableau]: Analyzed intern survey data and used logistic regression, and text mining. Created an 86% accurate model to predict offer acceptance. Came up with pragmatic recommendations for intern experience improvement.
4) Using Customer Behavior Data to Improve Customer Retention
o Objective: Telecom company concerned about revenues wishes to understand who and why are customers leaving
o Given: 7043 samples and 21 features - customers churn, services of each customer, account, and demographics
o [Python]: Ran logistic regression model on the dataset. Predicted the clients that were most likely to leave next and identified the features that are most important so that company could plan campaigns around retaining customers.
5) Retail Sales Prediction for a particular product
o Objective: Build a predictive model to predict sales of each product at a particular store to optimize overall sales
o Given: Sales data for 1559 products across 10 stores in different cities
o [Python]: Developed 90% accurate prediction model in Python using linear, lasso, ridge and polynomial regression
o Understood properties of products & stores playing key role in sales to develop expansion and go-to market plans

The University of Texas at Dallas May 2019
M.S., Business Analytics GPA 3.8/4
Dean’s Excellence Scholarship, Business Analytics Leadership Council, Big Data Club
Birla Institute of Technology and Science (BITS), Pilani - India Aug 2013
B.E.(Hons.), Manufacturing Engineering GPA 6.5/10
● Programming Skills: Python (numpy, pandas, scikit-learn, matplotlib, seaborn), R Programming, SQL, SAS
● Statistical Techniques: T-test, F-test, ANOVA, Hypothesis testing, A/B Testing, Distributions
● Data ScienceTechniques & Algorithms: Regression, Classification, Clustering, Decision trees, SVM, PCA, Ensemble Learning, Time Series Analysis, Integer Programming, Network Models, Queuing Theory, Feature Engineering
● Software: Tableau, Power BI, MS Excel

Master of Science in Information Systems