Harizon O.

Key Skills

Software

Clickworker

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Text

Top Task Types

Audio Recording

Classification

Cuboid

Freelancer Overview

I have extensive experience in data labeling and AI training data workflows, specializing in creating high-quality, structured datasets for machine learning models in NLP, computer vision, and multi-modal systems. My background includes annotating text for sentiment, intent, entity recognition, and conversational structure, as well as image and video labeling for object detection, classification, and fine-grained attribute tagging. I’m highly skilled at following detailed ontologies, maintaining labeling consistency, and using annotation tools such as Labelbox, Scale AI, SuperAnnotate, and custom internal platforms. My work is guided by accuracy, clarity, and a strong understanding of how labeled data impacts downstream model performance. Across projects, I’ve collaborated closely with data scientists, QA teams, and model developers to refine guidelines, resolve ambiguity, and improve annotation quality through iterative feedback loops. I stand out for my ability to detect edge cases, identify pattern inconsistencies, and suggest improvements that make datasets more robust and model-ready. Whether working independently or as part of a large team, I bring meticulous attention to detail, strong analytical skills, and a deep appreciation for the role high-quality data plays in building trustworthy AI systems.

Labeling Experience

AI Training Data Labeling Specialist – Multi-Modal Datasets

LabelboxImageBounding BoxSegmentation

Worked on multi-modal AI dataset annotation projects, including text, image, video, and audio labeling. Key tasks included: Annotating text for sentiment, intent, and named entities for chatbot and virtual assistant datasets. Classifying and labeling product images and videos for object detection, attribute tagging, and recommendation system training. Transcribing audio recordings and tagging intent for voice-based AI models. Ensuring annotation consistency through multi-annotator QA and resolving ambiguities in complex datasets. Collaborating with AI developers to refine labeling guidelines and optimize dataset quality for model training. The projects handled datasets of tens of thousands of entries, adhering to strict accuracy and quality standards.

2022 - 2023

Education

Work History