Ezekiel Offiong

Key Skills

Software

Label Studio

Remotasks

Top Subject Matter

Computer Vision

Natural Language Processing

Speech Recognition

Top Data Types

Image

Top Task Types

Bounding Box

Segmentation

Freelancer Overview

Data Labeling Specialist at Appen. Core strengths include Label Studio and Remotasks. Education includes Bachelor of Technology, Federal University of Technology, Minna (2027). AI-training focus includes data types such as Image, Medical, and DICOM and labeling workflows including Bounding Box and Segmentation.

Labeling Experience

Data Labeling Specialist at Appen

Label StudioImageBounding Box

I annotated over 150,000 images and video frames by applying bounding boxes, polygons, and semantic segmentation masks in support of machine learning models. I performed detailed NLP labeling tasks, such as sentiment analysis, named entity recognition, intent classification, and coreference resolution for language models. I consistently exceeded quality benchmarks, reviewed more than 5,000 peer annotations, and contributed to guideline creation for new team members. • Utilized platforms including Label Studio, CVAT, and Appen for scalable annotation work. • Contributed to audio transcription and speaker diarization datasets for speech recognition projects. • Maintained a personal accuracy rate of 98.5%. • Played a key role in defining annotation instructions and clarifying guidelines.

I annotated over 150,000 images and video frames by applying bounding boxes, polygons, and semantic segmentation masks in support of machine learning models. I performed detailed NLP labeling tasks, such as sentiment analysis, named entity recognition, intent classification, and coreference resolution for language models. I consistently exceeded quality benchmarks, reviewed more than 5,000 peer annotations, and contributed to guideline creation for new team members. • Utilized platforms including Label Studio, CVAT, and Appen for scalable annotation work. • Contributed to audio transcription and speaker diarization datasets for speech recognition projects. • Maintained a personal accuracy rate of 98.5%. • Played a key role in defining annotation instructions and clarifying guidelines.

2023 - Present

Junior Data Annotator at Remotasks

RemotasksSegmentation

I labeled medical imaging data, including X-rays and CT scans, following strict HIPAA-compliant workflows with pixel-level accuracy. I also processed and categorized text datasets for chatbot training and moderation, ensuring clarity and the removal of offensive content. High agreement rates were maintained with other annotators, and a diverse range of annotation platforms was utilized across projects. • Performed anatomy segmentation on medical images for clinical dataset enrichment. • Flagged and categorized ambiguous or offensive content in chatbot text datasets. • Trained on Labelbox, Scale AI, Appen, and Remotasks platforms for multi-domain labeling. • Also annotated autonomous driving datasets with lane, traffic sign, and pedestrian detection.

I labeled medical imaging data, including X-rays and CT scans, following strict HIPAA-compliant workflows with pixel-level accuracy. I also processed and categorized text datasets for chatbot training and moderation, ensuring clarity and the removal of offensive content. High agreement rates were maintained with other annotators, and a diverse range of annotation platforms was utilized across projects. • Performed anatomy segmentation on medical images for clinical dataset enrichment. • Flagged and categorized ambiguous or offensive content in chatbot text datasets. • Trained on Labelbox, Scale AI, Appen, and Remotasks platforms for multi-domain labeling. • Also annotated autonomous driving datasets with lane, traffic sign, and pedestrian detection.

2022 - 2023

Education

Work History