Oluwamuyiwa Fafowora

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Top Label Types

Computer Programming Coding

Freelancer Overview

I bring hands-on experience in data annotation, labeling, and AI training data workflows, having served as an AI Training Associate where I led efforts to create and optimize high-quality datasets for machine learning models. My work involved developing comprehensive training programs, collaborating with data scientists and engineers, and implementing best practices to ensure data accuracy and efficiency—resulting in a 40% increase in annotation productivity. I am skilled in using collaborative and project management tools such as Jira, Confluence, Slack, and Microsoft Teams, and have a strong technical foundation in Python and SQL databases. My background in IT system administration and cloud technologies further strengthens my understanding of data infrastructure, security, and automation, making me adept at delivering reliable, scalable training data solutions for AI projects.

Labeling Experience

AI Data Specialist

LabelboxComputer Code ProgrammingComputer Programming Coding

The Project involves creating a high-quality, realistic prompt (20,000–48,000 characters) with reusable instructions and domain-specific input data designed to challenge AI models into partial failure. Two AI model responses are generated from your prompt, and you then act as a domain expert to evaluate, rank, and justify which response is better based on objective rules, subjective quality, and scope adherence. The evaluation requires detailed, natural-sounding justifications comparing both responses' strengths and weaknesses, along with selecting a preference strength rating from "much better" to "no preference.

The Project involves creating a high-quality, realistic prompt (20,000–48,000 characters) with reusable instructions and domain-specific input data designed to challenge AI models into partial failure. Two AI model responses are generated from your prompt, and you then act as a domain expert to evaluate, rank, and justify which response is better based on objective rules, subjective quality, and scope adherence. The evaluation requires detailed, natural-sounding justifications comparing both responses' strengths and weaknesses, along with selecting a preference strength rating from "much better" to "no preference.

2024 - 2025

Education

Work History