Felix Oloyede

Key Skills

Software

Appen

Clickworker

Data Annotation Tech

LabelImg

Mercor

Remotasks

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Geospatial Tiled Imagery

Image

Text

Top Label Types

Action Recognition

Classification

Computer Programming Coding

Data Collection

Evaluation Rating

Geocoding

Mapping

Object Detection

Question Answering

Text Generation

Text Summarization

Freelancer Overview

I am a data analyst and AI training data specialist with strong experience in dataset preparation, annotation workflows, and quality assurance for machine learning models. I bring advanced SQL and data processing expertise, along with hands-on experience structuring, cleaning, and validating large datasets to meet strict AI training standards. My work aligns closely with AI training environments requiring precise labeling, guideline compliance, taxonomy development, and consistent quality control. I am experienced in designing annotation frameworks, conducting validation checks, reducing bias, and ensuring inter-annotator consistency to improve model accuracy and reliability. I understand how high-quality, well-documented datasets directly impact model performance across NLP and structured data tasks. With a background in ETL pipeline development, data governance, and cloud platforms (Azure and AWS), I contribute to scalable, production-ready AI workflows. I am detail-oriented, process-driven, and committed to delivering accurate, well-structured training data that supports continuous model improvement.

Labeling Experience

Facial Attribute Tagging and Image Categorization

AppenImageBounding Box

Annotated facial images to support computer vision model training by labeling visual attributes such as age group, gender presentation, facial expressions, accessories and other defined characteristics according to structured annotation guidelines. Performed multi-label classification and image categorization while ensuring consistency, accuracy, and compliance with data standards. Conducted quality checks to reduce bias and labeling errors, contributing to improved model performance in facial analysis and recognition tasks.

Annotated facial images to support computer vision model training by labeling visual attributes such as age group, gender presentation, facial expressions, accessories and other defined characteristics according to structured annotation guidelines. Performed multi-label classification and image categorization while ensuring consistency, accuracy, and compliance with data standards. Conducted quality checks to reduce bias and labeling errors, contributing to improved model performance in facial analysis and recognition tasks.

2025 - 2025

Education

Work History