Ugochukwu B.

Key Skills

Software

No software listed

Top Subject Matter

Industrial image datasets for AI model training

Structured and semi-structured pipeline text datasets for AI/ML

Industrial and operational image datasets for computer vision

Top Data Types

Image

Text

Document

Top Task Types

Classification

Freelancer Overview

Data Quality Specialist – Image Annotation QA. Core strengths include Internal and Proprietary Tooling. Education includes Professional Advanced Diploma, Smart Data Warehouse Solutions Institute (2024) and Diploma, Center for Bioethics Ibadan (2019). AI-training focus includes data types such as Image and Text and labeling workflows including Classification.

Labeling Experience

Data Quality Specialist – Image Annotation QA

ImageClassification

As a Data Quality Specialist, I conducted detailed frame-by-frame reviews to validate auto-generated image annotations. I systematically identified and corrected inaccurate, missing, or inconsistent image labels to uphold labeling and quality standards. The process ensured large-scale image datasets met accuracy and completeness requirements for downstream AI model ingestion. • Implemented structured image annotation QA workflows in line with project standards. • Maintained correction logs and quality metrics for continuous improvement. • Collaborated with AI developers to resolve complex annotation issues before submission. • Performed comprehensive reviews of auto-annotated industrial image data.

2022 - 2026

Data Pipeline Developer & QA Analyst – Text Annotation QA

TextClassification

In my role as Data Pipeline Developer & QA Analyst, I reviewed and corrected auto-generated labels in large-scale text data records. My work focused on identifying annotation errors, missing text classification labels, and inconsistencies introduced during automated processing. These corrections directly improved the quality and reliability of datasets used in AI/ML training workflows. • Applied systematic data validation and error detection processes to text datasets. • Produced clear documentation tracking annotation accuracy improvements. • Maintained high standards of label consistency across thousands of records. • Worked independently in a remote environment, emphasizing accuracy and detail.

2021 - 2022

AI Training Data Preparation & Quality Review – Image Annotation

ImageClassification

I specialized in preparing large-scale image datasets for AI/ML model training, focusing on accuracy and labeling standards. My responsibilities included reviewing, updating, and validating both original and auto-annotated image frames to guarantee overall label coherence. This methodical review contributed directly to increasing the integrity of training datasets and the performance of CV models. • Conducted thorough completeness and consistency checks on image labels. • Standardized annotation outputs using structured protocols and labeling guidelines. • Ensured dataset readiness for supervised learning and quality-controlled release. • Coordinated QA efforts on multi-batch annotation projects for industrial contexts.

2020 - 2021

Education

Work History