Data Science Specialist
Resume:
Mikhail Verghese
Data Scientist, B.Tech Computer Science Engineer
B.Tech Computer Science graduate with excellent interpersonal and communication
skills having lived and interacted in a multi-country, multi ethnic environment (India, Personal Info
Middle-East and USA). Currently residing in New Jersey, able to work for any employer
with no need for any visa sponsorship now or in the future. Interested in data analytics and machine learning opportunities.
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Experience
Technical Consultant
3i InfoTech Ltd.
I was involved with their ERP solution "Orion" and conducted market
research to identify new product development features for their North
American market transition.
Assembly Intern
Sysprotech India Pvt Ltd.
Oversaw PCB assembly of slot machines.
Volunteer Teacher
Ashadeep Foundation
Taught Advanced Mathematics and Calculus to under-privileged high Python
school students.
Education
2014-2018
B.Tech, Vellore Institute of Technology
Computer Science & Engineering
2012-2014
A Levels (Cambridge), Podar International School Data Visualization
High School
2018
College Final Year Project
Stock Market Prediction Program
Created a program to analyze stock market data and make near to accurate predictions on future prices, trends and price variations. Implemented on
Jupyter Notebooks using Python and concepts from data science and machine learning.
2018
College Third Year Project
Spam Filter Program
Developed a predictive classification model to detect spam sms messages
based on use of language and words. Using concepts from Natural Language
Processing (NLP).
2017
Personal Project
Weather Prediction Program
Developed a classification model using logistic regression to predict whether it would rain the following day, taking into consideration a number of factors
such as wind speed, wind direction etc.
2017
Personal Project
Movie Recommendation System
Developed a recommendation system using basic correlation comparison to suggest movies similar to that of which is selected. Carried out by comparing
ratings between specific viewers.
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