Joseph Obi

Key Skills

Software

Labelbox

CVAT

Prodigy

Top Subject Matter

General AI Model Training

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Top Data Types

Image

Text

Document

Top Task Types

Segmentation

Classification

Freelancer Overview

Data Annotation Specialist. Core strengths include Labelbox, CVAT, and Prodigy. Education includes Bachelor of Science, Federal University of Technology, Akure (2024). AI-training focus includes data types such as Image and labeling workflows including Segmentation and Classification.

Labeling Experience

Data Annotation Specialist

LabelboxImageSegmentation

As a Data Annotation Specialist, I labeled and validated over 1,000 data points daily with a focus on high accuracy. My work involved segmentation and classification techniques across large-scale image datasets. I contributed to increased precision and consistency in AI project outputs. • Maintained over 98% annotation accuracy. • Used Labelbox, CVAT, and Prodigy for image annotation. • Improved dataset consistency by 20% through QA processes. • Supported model precision and transcription accuracy improvements.

As a Data Annotation Specialist, I labeled and validated over 1,000 data points daily with a focus on high accuracy. My work involved segmentation and classification techniques across large-scale image datasets. I contributed to increased precision and consistency in AI project outputs. • Maintained over 98% annotation accuracy. • Used Labelbox, CVAT, and Prodigy for image annotation. • Improved dataset consistency by 20% through QA processes. • Supported model precision and transcription accuracy improvements.

2024 - Present

Data Quality Reviewer / QA Annotator

LabelboxImageClassification

As a Data Quality Reviewer and QA Annotator, I reviewed over 25,000 annotated entries for compliance with established standards. My responsibility was to identify labeling errors and enhance overall annotation quality. I collaborated with teams to promote consistency and accuracy in data labeling for AI models. • Reduced labeling errors by 22% using advanced QA methodologies. • Improved annotation consistency across large datasets. • Ensured quality compliance and process adherence. • Conducted thorough reviews using CVAT and Labelbox.

As a Data Quality Reviewer and QA Annotator, I reviewed over 25,000 annotated entries for compliance with established standards. My responsibility was to identify labeling errors and enhance overall annotation quality. I collaborated with teams to promote consistency and accuracy in data labeling for AI models. • Reduced labeling errors by 22% using advanced QA methodologies. • Improved annotation consistency across large datasets. • Ensured quality compliance and process adherence. • Conducted thorough reviews using CVAT and Labelbox.

2023 - 2024

Education

Work History