Bala Vivek

About Bala Vivek

As a data engineer at Capgemini, I develop and maintain data pipelines and solutions using Palantir Foundry, Workshop, Slate application, PySpark, SQL, Javascript and TypeScript- I have been part of analysis, design, development, and implementation of various initiatives, with end-to-end ownership to deliver the business functionalities- I have a master\'s degree in software systems from Birla Institute of Technology and Science, Pilani, and over 8 years of experience in software engineering and data analysis- Extensive experience in data modeling, data integration and processing of structured and unstructured data- I have acquired the AWS cloud practitioner certification- Previously, I was a development team lead and a senior software engineer at Accenture, where I worked on various projects involving machine learning, deep learning, and natural language processing and Linux environments- I have also earned multiple certifications from Coursera in data analysis, machine learning, and deep learning. I am passionate about solving complex data problems and delivering value to clients. I enjoy collaborating with cross-functional teams and learning new technologies and tools. I am always looking for opportunities to enhance my skills and contribute to the data engineering community.

Experience

1. Project Mywizard

   Accenture

   Dec 2017 — Present

   Prediction IO (PIO): PIO is an open-source tool for Machine Learning tool launched by Apache software foundation. Text classification uses the Navie Bayes Algorithm which exists as the template which produces the required result on passing Data. Created a tool called Accenture Ticket Resolver to provide high level automation in the area of Ticket management. Implemented Navie Bayes Algorithm using python, had good knowledge on Scikit learn, Text Blobs, Nltk libraries. Ereview : The tool is based on NLP where the exit employee reviews and comments are being processed. The tool outputs the Word cloud as a result for the HR. It provides the overview of the positive and negative comments over a Project level. The tool uses the preprocessing techniques to prevent the special characters, checks for the meaningful words, converts to lower case and finally converts the words to vectors using Word2Vec algorithm. Used the Randomforest Algorithm to predict the comment is positive or negative based on the training.

Education

• P.A. College Of Engineering

  Bachelor's Degree, Electrical, Electronics and Communications Engineering

  2014

• Birla Institute of Technology and Science, Pilani

  Master's degree, Software Systems

  2016 — 2018

• Brila Media

  Computer Software Technology/Technician

  2018

• P.A College of Engineering

  Bachelor's degree, Electrical, Electronics and Communications Engineering