Bryson Mercy

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Segmentation

Freelancer Overview

I am a detail-oriented freelancer with hands-on experience in AI data annotation, labeling, and quality assurance. My background includes classifying text data, tagging images, performing sentiment analysis, and validating datasets to ensure accuracy and consistency for AI and machine learning projects. I am skilled at following detailed annotation guidelines, working with large datasets, and using tools like Microsoft Excel and Google Sheets. With a foundation in computer science and a strong understanding of AI training workflows, I am comfortable with tasks such as named entity recognition, binary and multi-class classification, and internet research. I am committed to delivering high-quality results on time and am quick to learn new tools and platforms to meet evolving project needs.

Labeling Experience

AI DATA ANNOTATION

Data Annotation TechImageSegmentation

The project involved preparing high-quality labeled data to train machine learning models, focusing on tasks such as annotating images, text, or audio, applying bounding boxes or segmentation for visual data, and classifying or tagging each instance according to predefined categories. The team handled a dataset of [insert number, 50,00 items over three months, ensuring consistency and accuracy through inter-annotator agreement checks, random validation sampling, and strict adherence to labeling guidelines. Collaborative review and error-tracking processes were employed to maintain high data quality, supporting reliable AI model performance.

The project involved preparing high-quality labeled data to train machine learning models, focusing on tasks such as annotating images, text, or audio, applying bounding boxes or segmentation for visual data, and classifying or tagging each instance according to predefined categories. The team handled a dataset of [insert number, 50,00 items over three months, ensuring consistency and accuracy through inter-annotator agreement checks, random validation sampling, and strict adherence to labeling guidelines. Collaborative review and error-tracking processes were employed to maintain high data quality, supporting reliable AI model performance.

2023 - 2025

Education

Work History