Data Analysis, R, SAS, Python, Pipeline Pilot, Spotfire, Tableau

ADDITIONAL INFORMATION

Visa Status: EAD - Eligible to work in USA and will require H1B visa sponsorship after EAD (OPT) and STEM extension Gaurav Chaudhari

469-***-**** **********@*****.*** http://www.linkedin.com/in/gchaudhari https://github.com/gchaudhari EDUCATION

The University of Texas at Dallas (UT Dallas) December 2015 M.S., Management Information Systems Scholar of Recognition, GPA 3.76 Sardar Patel Institute of Technology, Mumbai University (S.P.I.T) May 2012 B.E., Electronics and Telecommunication First Class Honors, 6.3/10 Stanford University (MOOC Coursera) In Progress

Machine Learning

CERTIFICATIONS & TECHNICAL SKILLS

Analytical tools: SAS, R, MS SQL Server, SPSS, Tableau, WEKA, Spotfire, Pipeline Pilot Languages: Python, VBA, C++, VC++ (MFC & Win32), XML, XSL Application Software: MS Office, Eclipse, Matlab, Visual Studio, MS Excel advance, MS Access, MS Visio, Toad Oracle Certifications: SAS-UTD Graduate Certificate in Data Mining and Business Intelligence, Financial Markets & Equity Derivatives Machine Learning: Random Forest, XGBoost, Support Vector Machine, Caret Package, Regression, Latent Class clustering BUSINESS EXPERIENCE

Monsanto – Rose International, Chesterfield, MO March 2016 – Present Logistic Data Analyst II, Full Time

Led the development of prescriptive data model for corn shelling to optimize operations and increase packaging efficiency

Designed and maintained Spotfire visualization dashboards on daily basis to facilitate Field testing all over North America

Developed interface for 2017 Plot Allocation to decrease 1 month of work load using Pipeline Pilot

Forecasted field resource requirements for FY 2017 and prescribed optimal solutions using historic financial data

Created a workforce Head Count analysis report using FY 2015-2017 data for the leadership teams at Monsanto

Fine-tuned and improved query performance by building query using Toad to increase time efficiency by 40% The Walt Disney Company, Burbank, CA January 2015 – July 2015 Management Science & Integration Intern, Full Time

Applied Marketing analytics in an internal consulting team of 13 to propose marketing campaign strategies to Walt Disney Studios and ABC Television

Initiated ETL on social intent data and created a predictive forecasting model which achieved a 10% improvement in estimation of Gross Box Office for 192 movies over period of 5 years using SAS

Saved 85% of reporting process time by creating, automating and maintaining standardized templates in Excel and Tableau

Analyzed, improved and streamlined the process of forecasting premier ratings for FY 2015 ABC Television to improve media planning by leveraging marketing mix model using SAS

Performed Post mortem analysis using SAS and R to create a presentation report for Director to demonstrate ROI increase to key Disney Studios stakeholders

Developed a program in R to visualize, validate and automate the results of a sampling tool that synthesizes campaign data for long range forecasts and provided strategic recommendations to management Geometric LTD., Mumbai, India January 2013 – May 2013 Trainee, Full-Time

Implemented visual enhancements recommended by 4 cross functional managers to improve CamWorks 2014 User Interface

Evaluated a new product and provided purchase recommendations to management ACADEMIC PROJECTS

Predictive Analytics using SAS Project for VCA Animal hospital August 2015 – December 2015

Analyzed and segmented VCA clinic data using Latent class and prescribed recommendations using Price Elasticity model

Uncovered new insights using demographics and spend data by applying Linear Regression Analysis and investigating the model for fitting/overfitting, significance by ruling out problems like Multicollinearity, Heteroscedasticity and Autocorrelation Advanced Business Intelligence Project for eCRM (Customer Analytics) August 2014 – December 2014

Leveraged data preprocessing techniques (Normalization, Discretization) and created advanced models (Naïve Bayes, Bayesian net, support vector machine) on Expedia dataset and computed optimum model for the dataset in WEKA

Integrated model enhancement techniques (Gradient boosting, imputation) with various advance models (Neural network, Regression, Decision Tree) and generated prediction booking model for Expedia dataset with 85% accuracy in SAS Miner

Contact this candidate