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Oluwatobi Daniels

Oluwatobi Daniels

Data Entry Specialist - AI and Cybersecurity

NIGERIA flag
Lagos, Nigeria
$40.00/hrIntermediateOtherInternal Proprietary Tooling

Key Skills

Software

Other
Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

DocumentDocument
TextText

Top Label Types

Classification
Data Collection
Entity Ner Classification
Evaluation Rating
RLHF

Freelancer Overview

I am a detail-oriented Data Entry Specialist and AI/ML Research Analyst with over 4 years of hands-on experience in data annotation, labeling, and structured data management. My background includes extensive work with NLP datasets, classification tasks, and RLHF-style model feedback for both academic research and industry projects. I have curated, cleaned, and labeled large volumes of data, including fake news detection, social media analysis, and cybersecurity literature, using tools such as Python, Scikit-Learn, Power BI, and Tableau. My experience spans building and automating data pipelines, ensuring data quality and consistency, and supporting AI model training through precise annotation and benchmarking. I thrive in remote, asynchronous environments and am committed to delivering high-accuracy, well-documented outputs that support robust AI and machine learning solutions.

IntermediateEnglish

Labeling Experience

Federated Learning Model Output Evaluation & Preference Ranking

OtherTextEvaluation RatingData Collection
Evaluated and ranked model outputs across privacy-utility tradeoff scenarios in a Vertical Federated Learning research project. Tasks included assessing response quality, accuracy, and behavioral alignment with expected outcomes — directly mirroring RLHF-style preference ranking workflows. Maintained detailed evaluation logs and applied consistent scoring criteria across experimental runs. Manuscript currently under peer review.

Evaluated and ranked model outputs across privacy-utility tradeoff scenarios in a Vertical Federated Learning research project. Tasks included assessing response quality, accuracy, and behavioral alignment with expected outcomes — directly mirroring RLHF-style preference ranking workflows. Maintained detailed evaluation logs and applied consistent scoring criteria across experimental runs. Manuscript currently under peer review.

2025

AI & Cybersecurity Research – Structured Literature Annotation

Internal Proprietary ToolingTextEntity Ner Classification
Systematically reviewed and annotated 100+ peer-reviewed papers on AI and cybersecurity, tagging each by threat category, AI technique applied, and position along the Cyber Kill Chain framework. Maintained a structured annotation database ensuring consistent label application across all entries. Annotations directly supported a comparative review published in the University of Ibadan Journal of Science and Logics in ICT Research (2025).

Systematically reviewed and annotated 100+ peer-reviewed papers on AI and cybersecurity, tagging each by threat category, AI technique applied, and position along the Cyber Kill Chain framework. Maintained a structured annotation database ensuring consistent label application across all entries. Annotations directly supported a comparative review published in the University of Ibadan Journal of Science and Logics in ICT Research (2025).

2025 - 2025

COVID-19 Fake Tweet Classification – NLP Text Annotation

OtherTextClassificationData Collection
Annotated a dataset of COVID-19 tweets, labeling each sample as real or fake based on defined credibility criteria and source verification guidelines. Designed annotation rules to ensure consistency, handled class imbalance across labels, and iteratively refined the taxonomy to improve inter-annotator agreement. The labeled dataset was used to train and evaluate multiple ML classifiers. Work resulted in a peer-reviewed publication in JOSTMED (2023).

Annotated a dataset of COVID-19 tweets, labeling each sample as real or fake based on defined credibility criteria and source verification guidelines. Designed annotation rules to ensure consistency, handled class imbalance across labels, and iteratively refined the taxonomy to improve inter-annotator agreement. The labeled dataset was used to train and evaluate multiple ML classifiers. Work resulted in a peer-reviewed publication in JOSTMED (2023).

2020 - 2021

Education

U

University of Lagos

Masters Degree, Computer Science

Masters Degree
2025 - 2025
F

Federal University of Technology Minna

Bachelor of Technology, Computer Science

Bachelor of Technology
2017 - 2021

Work History

C

CBC Gedu Technologies Ltd.

Cybersecurity Engineer | Data & Research Analyst

Lagos
2021 - Present