Personal details

Rajeev K. - Remote data scientist

Rajeev K.

Based in: 🇼🇳 India
Timezone: Mumbai (UTC+5.5)

Summary

Data Scientist having 5 years of experience skilled in Machine Learning , Deep Learning , Statistics , problem-solving and programming .
I have deep knowledge of several languages and Library such as Python(Keras , Scikit-Learn, pandas, Numpy etc) .
I have worked on Classification , Regression , Deep Learning and CNN projects.

Mentoring to the new joiner in my project and taking cross-skilling session after every quarter.

During my journey , I successfully combined my studies with work and other commitments showing myself to be self-motivated , organised and capable of working under pressure .
I have a clear , logical mind with a practical approach to problem-solving and drive to see things through to completion .

Work Experience

Senior Analyst - Data Science
Tiger Analytics | May 2022 - Present
Machine Learning
Nltk
Python 3
Data Science
Working as a Data Scientist in Tiger Analytics.
Machine Learning Engineer
TATA CONSULTANCY SERVICES | Jul 2018 - Apr 2022
Python
Machine Learning
NLP (Natural Language Processing)
Data Visualization
Deep Learning
Handling daily activity of Data Analysis , Data Visualisation , Data Pre-processing , Feature Engineering, Feature Selection , Hyper-Parameter tuning , Model Building using Regression Technique , Classification Technique, NLP, Deep-Learning , CNN . Mentoring to the new joiner in my company and taking cross-skilling session after every quarter.

Personal Projects

Tweet Sentiment Analysis
2021
Python
Machine Learning
Data Visualization
Applied NLP Concept to pre-process the text data . Applied Machine Learning concept to build sentiment analysis-based NLP model that can classify the sentiment of tweet into Positive , Negative & Neutral . Secured 5th Rank in this Hackathon .
Model to predict that whether system is compromised or notIconOpenNewWindows
2020
Python
Machine Learning
Data Visualization
In 2020 there was a cyber attack on SolarWinds . Our Company also used SolarWinds product for infrastructure support . Our leadership asked to build a machine learning model to detect whether the system is compromised or not . Used Recall as evaluation metric because the cost of making mistakes would have huge impact. Achieved accuracy of 99.53 %.