CHINONYE E. EZENWATA

Data Annotation Specialist • AI/ML Data Labeling Expert

Remote, NG.

About

Highly detail-oriented AI/ML Data Labeling Expert with over 5 years of hands-on experience in multimodal data annotation, specializing in image, video, and audio analysis. Proficient across industry-leading platforms like CVAT, Labelbox, and Scale AI, consistently delivering high-accuracy datasets crucial for advancing machine learning models. Proven ability to meet rigorous project deadlines and maintain stringent quality standards as a 100% remote contributor, ensuring optimal data integrity and model performance.

Work

Multiple Platforms

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Freelance Data Annotation Specialist

Remote, Nigeria, Nigeria

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Summary

Currently spearheading diverse data annotation projects for computer vision and NLP models, ensuring high-quality dataset delivery across multiple platforms.

Highlights

Completed over 500 complex annotation tasks across image, video, and audio datasets, directly supporting advanced computer vision and NLP model training initiatives.

Achieved and maintained top-tier annotator ratings on Scale AI and Appen platforms by consistently upholding rigorous quality standards and precision.

Executed highly accurate semantic segmentation on autonomous driving datasets, consistently maintaining 97% accuracy scores.

Collaborated effectively with distributed QA teams to resolve annotation ambiguities and refine labeling guidelines, enhancing overall data quality and project efficiency.

Delivered specialized audio transcription and voice recording tasks for speech recognition AI pipelines, contributing to robust language model development.

Appen / Remotasks

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Data Labeling Contractor

Remote, Nigeria, Nigeria

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Summary

Successfully labeled multimodal training data for major AI clients, contributing to the development of advanced machine learning models.

Highlights

Labeled diverse multimodal training data, including images, videos, and short audio clips, for major AI clients, enhancing model accuracy and performance.

Participated in complex video annotation projects, performing frame-by-frame object tracking and activity recognition with high precision.

Reviewed and validated peer annotations to ensure comprehensive dataset consistency, critically impacting model training cycles.

Demonstrated rapid adaptability by smoothly onboarding to over 10 different labeling tasks, consistently meeting diverse project guidelines and requirements.

Education

University of Lagos

Lagos, Nigeria, Nigeria
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M.Sc. Artificial Intelligence & Data Science

Courses

Thesis: Multimodal Data Annotation Pipelines for Scalable Machine Learning, evaluating inter-annotator agreement and label quality optimization across image, video, and text datasets.

Key Modules: Deep Learning for Computer Vision, Natural Language Processing, Human-in-the-Loop AI Systems, Research Methods in Data Science, Responsible AI & Data Ethics.

Developed an end-to-end annotation quality assessment framework, benchmarking performance across three major labeling platforms.

Nnamdi Azikiwe University Awka

Awka, Nigeria, Nigeria
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B.Sc. Computer Science / Information Technology

Courses

Introduction to Machine Learning & AI, covering supervised/unsupervised learning, feature engineering, model evaluation, and foundational computer vision.

Coursework and self-study in Machine Learning fundamentals, Computer Vision, and AI data pipelines.

Final Year Project: Automated Image Classification System; built and trained a CNN model using manually annotated dataset, demonstrating critical impact of label quality.

Certificates

Computer Vision & Image Recognition (in progress)

Coursera / deeplearning.ai

HIPPA Certification

Data Annotation Fundamentals

Microsoft

Quality Assurance for AI Datasets

Internal Appen Training

Remote Work & Digital Collaboration Tools

Cosera

Skills

Data Modalities

Image, Video, Audio/Speech, Text, Multimodal AI Datasets, CT Scans, MRI Scans.

Annotation Platforms

CVAT, Labelbox, Scale AI, Appen, Remotasks, Telus, iMerit, Transcribe Me.

Annotation Types

Bounding Boxes, Polygon Segmentation, Keypoint/Pose Estimation, Semantic Labeling, Video Tracking, Scene Analysis, Object Detection, Depth Estimation, Weather Classification, Object Relationship Labeling, Frame-by-Frame Object Tracking, Activity Recognition, Linguistic Annotations, Intent Tagging, Entity Labeling, Sentiment Classification.

Quality Assurance

Inter-annotator Agreement, QA Review Workflows, Error Rate Analysis, Annotation Guidelines Authoring, Dataset Consistency, Data Quality Validation.

AI/ML Concepts

Computer Vision, Natural Language Processing (NLP), Machine Learning, Deep Learning, Supervised Learning, Unsupervised Learning, Feature Engineering, Model Evaluation, AI Data Pipelines, Human-in-the-Loop AI Systems, Responsible AI & Data Ethics.

Technical Tools

Google Workspace, Airtable, Slack, Zoom, JSON/CSV Data Handling.

Other Skills

Transcription, Voice Recording, Remote Collaboration, Project Management, Data Confidentiality, HIPAA Compliance, Adaptability, Problem-Solving.

Projects

Autonomous Vehicle Perception Dataset

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Summary

A critical computer vision project focused on enhancing perception capabilities for autonomous vehicles through high-precision data annotation.

Conversational AI Voice Dataset

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Summary

An NLP/Speech Recognition project aimed at building diverse and high-quality voice datasets for multilingual AI assistant training.

Medical Imaging Annotation

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Summary

A healthcare AI project involving the precise annotation of medical imaging data under strict protocols to support diagnostic model development.