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S

Samuel Babalola

AI Data Annotator

Nigeria flagIbadan, Nigeria
$10.00/hrIntermediateCVATProdigyRoboflow

Key Skills

Software

CVATCVAT
ProdigyProdigy
RoboflowRoboflow
AWS SageMakerAWS SageMaker

Top Subject Matter

Health
Technology
Agriculture

Top Data Types

ImageImage
VideoVideo
TextText

Top Task Types

Bounding BoxBounding Box
PolygonPolygon
ClassificationClassification
Entity (NER) ClassificationEntity (NER) Classification
SegmentationSegmentation
Object DetectionObject Detection
Evaluation/RatingEvaluation/Rating

Freelancer Overview

Detail-oriented data professional with a Master of Science in Data and Information Science (Distinction) from the University of Ibadan (2025) and a Bachelor of Science from Obafemi Awolowo University (2019). With hands-on experience in data processing, content evaluation, and quality assurance since January 2024, I have consistently processed and categorized large datasets with high accuracy, evaluated online content for quality and relevance, and followed complex annotation guidelines to ensure consistency across diverse datasets. My background in data management, combined with strong proficiency in Python, Excel, Google Sheets, and SPSS, equips me with the technical foundation needed to deliver reliable and precise AI training data. I am experienced in remote, independent work environments, capable of meeting strict deadlines while maintaining the high accuracy standards that data labeling and AI training projects demand

IntermediateEnglishYoruba

Labeling Experience

Crop Disease Detection – Image Annotation Specialist

ImagePolygon
Performed polygon-based image annotation on crop photographs to support the development of an AI-powered disease detection model. Tasks included accurately labelling diseased regions on plant leaves, stems, and fruits across multiple crop types, while distinguishing between healthy and infected areas. Maintained high annotation quality by following detailed labeling guidelines, ensuring consistency across large image datasets, and adhering to quality control standards. Work directly contributed to training machine learning models capable of identifying and classifying crop diseases with greater precision.

Performed polygon-based image annotation on crop photographs to support the development of an AI-powered disease detection model. Tasks included accurately labelling diseased regions on plant leaves, stems, and fruits across multiple crop types, while distinguishing between healthy and infected areas. Maintained high annotation quality by following detailed labeling guidelines, ensuring consistency across large image datasets, and adhering to quality control standards. Work directly contributed to training machine learning models capable of identifying and classifying crop diseases with greater precision.

2026 - 2026

Medical Symptom Report Classification

TextClassification
Performed text classification on large volumes of medical symptom reports, patient intake forms, and clinical notes to support the development of an AI-powered diagnostic assistance model. Tasks involved reading and categorizing text entries into predefined medical classes such as respiratory conditions, cardiovascular symptoms, neurological complaints, gastrointestinal disorders, and general wellness indicators. Applied strict annotation guidelines to ensure accurate and consistent labeling across thousands of text samples, with particular attention to ambiguous or overlapping symptom descriptions. Conducted regular self-review and quality checks to maintain high inter-annotator agreement scores, minimizing label noise in the training dataset.

Performed text classification on large volumes of medical symptom reports, patient intake forms, and clinical notes to support the development of an AI-powered diagnostic assistance model. Tasks involved reading and categorizing text entries into predefined medical classes such as respiratory conditions, cardiovascular symptoms, neurological complaints, gastrointestinal disorders, and general wellness indicators. Applied strict annotation guidelines to ensure accurate and consistent labeling across thousands of text samples, with particular attention to ambiguous or overlapping symptom descriptions. Conducted regular self-review and quality checks to maintain high inter-annotator agreement scores, minimizing label noise in the training dataset.

2025 - 2025

Education

U

University of Ibadan

Master of Science, Data and Information Science

Master of Science
2024 - 2025
O

Obafemi Awolowo University

Bachelor of Science, Animal Science

Bachelor of Science
2015 - 2019

Work History

H

Herrand Solutions Ltd

Data Analyst & Content Evaluator

Abuja
2023 - 2025