Faruq Fakayode

Key Skills

Software

CVAT

Label Studio

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Medical Dicom

Text

Video

Top Label Types

Segmentation

Classification

Diagnosis

Evaluation Rating

Freelancer Overview

I am a detail-oriented AI Data Labeler and Annotation Specialist with extensive experience annotating and labeling multimodal datasets—text, image, audio, and medical data—for machine learning and AI model development. My background in biomedical engineering and data science allows me to deliver high-accuracy training data, particularly in domains such as computer vision, NLP, and medical diagnostics. I am proficient with industry-standard platforms like Label Studio, Labelbox, CVAT, and Remotasks, and skilled in Python-based data preprocessing and automation. I have consistently maintained over a 98% quality score across 50,000+ labeled data points, contributed to published biomedical AI research, and authored annotation guidelines that improved team accuracy. My expertise spans NER, sentiment analysis, intent classification, bounding box annotation, RLHF evaluation, and medical image labeling, and I thrive in collaborative, quality-focused environments.

Labeling Experience

Medical Imaging Annotation for AI-Assisted Diagnostic System (ECG & MRI)

Scale AIMedical DicomSegmentationClassification

Led the annotation and quality review of a 15,000+ sample medical imaging dataset comprising ECG signal plots and MRI scans for a clinical AI diagnostic research project at the University College Hospital, Ibadan. Responsibilities included pixel-level segmentation of anatomical structures in MRI images, classification of ECG rhythms into diagnostic categories (normal sinus, atrial fibrillation, ventricular tachycardia, etc.), and evaluation/rating of model predictions against ground-truth clinical labels. Applied DICOM-aware annotation workflows using Label Studio and CVAT, ensuring HIPAA-compliant data handling and strict inter-annotator agreement protocols. Achieved a >98% agreement score across the annotation team and contributed labeled data that directly supported a peer-reviewed publication on AI-assisted cardiac diagnostics.

Led the annotation and quality review of a 15,000+ sample medical imaging dataset comprising ECG signal plots and MRI scans for a clinical AI diagnostic research project at the University College Hospital, Ibadan. Responsibilities included pixel-level segmentation of anatomical structures in MRI images, classification of ECG rhythms into diagnostic categories (normal sinus, atrial fibrillation, ventricular tachycardia, etc.), and evaluation/rating of model predictions against ground-truth clinical labels. Applied DICOM-aware annotation workflows using Label Studio and CVAT, ensuring HIPAA-compliant data handling and strict inter-annotator agreement protocols. Achieved a >98% agreement score across the annotation team and contributed labeled data that directly supported a peer-reviewed publication on AI-assisted cardiac diagnostics.

2021 - 2024

Education

Work History