Data Engineering

HETUL VARAIYA

Address: **** * ********** **, *****, AZ-85281 LinkedIn: linkedin.com/in/hvaraiya

e-mail: ********@***.*** Github: https://github.com/hetulrv Phone: 480-***-****

SUMMARY:

Highly motivated Industrial Engineering Graduate with deep interest in Business and Data Analytics. Experience in working on Medical, Housing and other Business Data to provide insights and KPIs to help with Business Process analysis and development. Experience with SQL, Tableau, Python and Relational Databases. EDUCATION:

Master of Science in Industrial Engineering (MSIE) GPA – 3.2 / 4 May 2018 Arizona State University (ASU), Ira A. Fulton School of Engineering, Tempe, AZ Bachelor of Technology, Instrumentation and Control Engineering GPA – 3.57 / 4 May 2015 Institute of Technology, Nirma University, Ahmedabad, India TECHNICAL SKILLS AND RELEVANT COURSES

• Languages: R, SAS, SPSS, Python - Numpy, Pandas, Scikit-Learn, seaborn, UI/UX

• Statistical and Database Tools: JMP, Minitab, MySQL, MS SQL server, Weka, SQL Server Reporting tools(SSRS), MySQL, VBA

• ETL Tools: Informatica, Cognos, Tableau, Power BI, Ad-hoc Reporting, Familiar with AWS infrastructure

• Relevant Courses: Design and Analysis of Experiment, Information Systems Engineering, Production Systems, Regression Analysis, Data-Mining, Computational Statistics, Certified Lean Six Sigma Green Belt, Time Series Forecasting, Data Visualization, Advanced Quality Control, Data Science for System Informatics. EXPERIENCE

Data Mining Research Assistant, Laubichler Labs, Arizona state University, Tempe October 2017 – May 2018 (8 Mos)

● Improved the Data Collection, Data Cleaning, A/B testing, Hypothesis Testing, Data Consolidation and Exploratory Data Analysis of the medical Health Data by reducing redundancy and Multicollinearity in the dataset using Python

● Built Classification models to gain useful insights from a Medical Health data.

● The analysis showed the ROC Curve and Area Under the Curve as the performance metrics and the probability of a person getting a stroke was predicted by building models that had the highest accuracy. Business/Business Intelligence Analyst, Infosys Limited, Mysore, India October 2015 – May 2016(8 Mos)

• Created reports having real time visualizations based on the supply chain and logistics data of a company using BI tools like Informatica(T-SQL), PowerBI and Tableau to provide insights for the optimization of the store operation process and replenishment of the products in the retail chain(D-Mart).

● Designed an analytical model to show an improvement in the store operation process by 15%. Process Automation Engineering Intern, Atul Chemicals Limited, India May 2014 – Dec 2014 (9 Mos)

● Developed an application used to generate reports using SSRS. Conducted analysis, design, manufacturing and installation of the different level and temperature sensors into the junction boxes used in the manufacturing of the resins.

• Improved the revenue of the company by 7% by reducing the waste generated during the manufacturing of resins. PROJECTS

Prediction of the race of a person from the supervised data, Arizona State University Summer 2017

• With the use of different machine learning algorithms such as Support Vector Machines, Neural Networks, Naïve Bayes and Decision Trees a dataset with 20 attributes and 6500 instances/objects were trained using ensemble method and supervised learning for the classification of the data.

• The trained model was then used for the non-classified Data to predict the Race (Class) of the person from the test data using the other attributes. The efficiency for the model obtained was 84%. Human Resources Management at Sterling Hospital, Arizona State University Spring 2017

• Created the Entity-Relationship Diagrams, Relational models for the relational databases obtained from Sterling Hospital.

• Created an application using VBA and MS-SQL server to create a database management system for the hospital employees. Neural Network based character recognition system, Nirma University Spring 2015

• Developed an application to recognize the letters and number plates of the cars and buses by training the nntool with the data collected from the handwritten as well as the number plates of thousands of cars

• Improved the efficiency of the model by changing the number of epochs and other parameters.

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