Ferdinand Joshua

Key Skills

Software

CVAT

Top Subject Matter

Healthcare - Medical Records & Patient Data

Finance - Risk Analysis & Fraud Detection

E - Commerce - Product Categorization & Customer Support

Top Data Types

Image

Text

Top Label Types

Text Summarization

Bounding Box

Freelancer Overview

I am a Data Labeling & Quality Assurance Specialist with over 5 years of experience in structured annotation, metadata tagging, and dataset validation for AI and machine learning projects. My work spans image, text, and metadata datasets, where I have consistently delivered high‑accuracy annotations that improve model training outcomes. Skilled in industry‑standard platforms such as Labelbox and CVAT, I combine technical proficiency with strong attention to detail to ensure reliable, high‑quality datasets. Throughout my career, I have annotated and audited more than 15,000 datasets with a 98% accuracy rate, collaborated with engineers to refine labeling guidelines, and implemented quality control measures that reduced error rates by 20%. My background in data analytics and machine learning fundamentals further strengthens my ability to understand project requirements and contribute to AI model development. I bring a proven track record of precision, adaptability, and teamwork to every project.

Labeling Experience

Expert Data Labeling & Quality Assurance Specialist

CVATImageText Summarization

Led a large‑scale annotation project involving over 15,000 product images for an AI‑powered retail recognition system. Using Labelbox and CVAT, I applied bounding boxes and polygon segmentation to classify items with a 98% accuracy rate. Collaborated with engineers to refine annotation guidelines, reducing error rates by 20% and improving dataset reliability. My role included metadata tagging, dataset validation, and quality assurance, ensuring high‑quality training data that enhanced model performance.

Led a large‑scale annotation project involving over 15,000 product images for an AI‑powered retail recognition system. Using Labelbox and CVAT, I applied bounding boxes and polygon segmentation to classify items with a 98% accuracy rate. Collaborated with engineers to refine annotation guidelines, reducing error rates by 20% and improving dataset reliability. My role included metadata tagging, dataset validation, and quality assurance, ensuring high‑quality training data that enhanced model performance.

2023 - 2025

Expert Data Labeling & Quality Assurance Specialist

ImageBounding Box

Scope of the project: Large‑scale annotation across image, text, and metadata datasets for AI and machine learning projects. Specific tasks performed: Image annotation (bounding boxes, polygons, segmentation masks) for retail and medical datasets. Text classification and categorization for NLP projects (sentiment, chatbot training). Metadata tagging and dataset documentation. Quality assurance audits and error correction. Project size: Annotated and audited 15,000+ datasets (images and text), plus thousands of metadata records. Quality measures adhered to: Achieved 98% accuracy rate in annotations. Implemented corrective measures that improved model training outcomes by 12%. Collaborated with engineers to refine guidelines, reducing error rates by 20%. Designed QA workflows to ensure compliance with project standards.

Scope of the project: Large‑scale annotation across image, text, and metadata datasets for AI and machine learning projects. Specific tasks performed: Image annotation (bounding boxes, polygons, segmentation masks) for retail and medical datasets. Text classification and categorization for NLP projects (sentiment, chatbot training). Metadata tagging and dataset documentation. Quality assurance audits and error correction. Project size: Annotated and audited 15,000+ datasets (images and text), plus thousands of metadata records. Quality measures adhered to: Achieved 98% accuracy rate in annotations. Implemented corrective measures that improved model training outcomes by 12%. Collaborated with engineers to refine guidelines, reducing error rates by 20%. Designed QA workflows to ensure compliance with project standards.

2019 - 2025

Education

Work History