Moses Mwangi

Key Skills

Software

Label Studio

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Label Types

Classification

Transcription

Freelancer Overview

I am a software developer with hands-on experience in data annotation, data labeling, polygon and video annotation, and QA testing, utilizing tools such as Label Studio and Roboflow. My background includes building data-driven applications, integrating APIs, and handling both structured and unstructured data across domains like e-commerce, logistics, and real-time tracking. I have a strong foundation in Python and JavaScript, and I am actively transitioning into AI-focused engineering by applying my skills in automation, data handling, and systems thinking to intelligent applications. My experience working with modern tech stacks and cloud platforms enables me to efficiently manage and prepare high-quality training data for AI and machine learning projects.

Labeling Experience

Customer Feedback Sentiment Annotation Project

Label StudioTextClassification

Manually collected and annotated 800+ real customer reviews from public sources and classified them into Positive, Neutral, and Negative sentiment categories to support natural language processing model training. Developed detailed labeling guidelines to ensure consistency and reduce subjectivity during annotation. Performed data cleaning, duplicate removal, and class balancing to improve dataset quality. Conducted quality assurance through manual review and validation of ambiguous entries. Final dataset was structured in CSV format and published with documentation for use in sentiment analysis and machine learning applications.

Manually collected and annotated 800+ real customer reviews from public sources and classified them into Positive, Neutral, and Negative sentiment categories to support natural language processing model training. Developed detailed labeling guidelines to ensure consistency and reduce subjectivity during annotation. Performed data cleaning, duplicate removal, and class balancing to improve dataset quality. Conducted quality assurance through manual review and validation of ambiguous entries. Final dataset was structured in CSV format and published with documentation for use in sentiment analysis and machine learning applications.

2025

Education

Work History