Rajeev Kumar

Key Skills

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Other

Labelbox

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Computer Code Programming

Document

Text

Top Label Types

Computer Programming Coding

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Freelancer Overview

With over 3.5 years of experience in data analytics and automation, I have developed a deep understanding of how high-quality data fuels AI systems and business intelligence. My background includes building ETL pipelines, automating data workflows, and developing dashboards that transform raw datasets into actionable insights. At Gamut Analytics, I streamlined campaign operations using Python and Power BI, ensuring accurate and consistent labeling and reporting across multiple client portfolios. I bring a strong technical foundation in Python (Pandas, NumPy, Flask), SQL, and data visualization tools, along with hands-on exposure to NLP and MongoDB analytics. My analytical mindset, combined with experience in handling large datasets and ensuring data integrity, positions me well to contribute effectively to AI training data, annotation projects, and data quality improvement initiatives.

Labeling Experience

Social Media Sentiment Labeling and Conversational AI Annotation

LabelboxTextEntity Ner ClassificationText Generation

This project involved annotating and validating large volumes of text and conversational data to support sentiment analysis and chatbot model training. Tasks included manual and semi-automated labeling of text sentiment (positive, negative, neutral), identifying entities and intents in chat transcripts, and validating AI-generated responses for tone and accuracy. I developed Python automation scripts to cross-check data consistency, preprocess text, and ensure labeling accuracy across datasets. Over 50,000 text entries were annotated and validated, resulting in an improved model accuracy of 82%. Quality assurance was maintained through multiple review cycles and inter-annotator agreement metrics.

This project involved annotating and validating large volumes of text and conversational data to support sentiment analysis and chatbot model training. Tasks included manual and semi-automated labeling of text sentiment (positive, negative, neutral), identifying entities and intents in chat transcripts, and validating AI-generated responses for tone and accuracy. I developed Python automation scripts to cross-check data consistency, preprocess text, and ensure labeling accuracy across datasets. Over 50,000 text entries were annotated and validated, resulting in an improved model accuracy of 82%. Quality assurance was maintained through multiple review cycles and inter-annotator agreement metrics.

2020 - 2023

Education

Work History