Hesbon Nyobuoro

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Entity Ner Classification

Freelancer Overview

I am a detail-oriented Data Annotation Specialist with over 3 years of experience in data labeling, transcription, and quality assurance for AI and machine learning projects. My expertise spans frame-by-frame video annotation, audio recording, voice acting, object tracking, semantic segmentation, bounding boxes, polygon labeling, audio transcription, and text classification (NER), with a proven record of annotating 10,000+ data points at 99%+ accuracy. I have worked on multilingual projects involving English, Swahili, and Zulu, contributing to speech recognition and NLP systems by capturing linguistic nuances and improving model accuracy. My strong QA background includes managing bug tracking and resolution with JIRA, maintaining project documentation, and driving process improvements to ensure high-quality, consistent training data. I thrive in deadline-driven, collaborative environments and am adept at adapting to new annotation tools and workflows to meet evolving project needs.

Labeling Experience

NLP Text Annotation for Sentiment Analysis & Language Modeling

Data Annotation TechTextEntity Ner Classification

Annotated over 10,000 sentences for NLP model training, focusing on syntactic structure, semantic meaning, and sentiment analysis. Tasks included part-of-speech tagging, named entity recognition, and emotion classification. Applied strict annotation guidelines to ensure consistency across the dataset. Collaborated with cross-functional teams to refine labeling rules. The high-quality data contributed to a 12% improvement in model accuracy for sentiment analysis and language understanding tasks.

Annotated over 10,000 sentences for NLP model training, focusing on syntactic structure, semantic meaning, and sentiment analysis. Tasks included part-of-speech tagging, named entity recognition, and emotion classification. Applied strict annotation guidelines to ensure consistency across the dataset. Collaborated with cross-functional teams to refine labeling rules. The high-quality data contributed to a 12% improvement in model accuracy for sentiment analysis and language understanding tasks.

2020 - 2023

Education

Work History