William, Edidiong

Key Skills

Software

Mindrift

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Document

Image

Text

Video

Top Label Types

Evaluation Rating

Prompt Response Writing SFT

Text Summarization

Transcription

Freelancer Overview

I am a detail-oriented data analyst and researcher with hands-on experience in data annotation, quality review, and AI training data evaluation. Through my work with Mindrift, I have assessed AI-generated content for accuracy, relevance, and safety, ensuring alignment with strict evaluation criteria and providing structured feedback to improve model performance. My freelance projects on Upwork have further strengthened my ability to follow detailed instructions and deliver high-quality labeled data on time. I am proficient in Python, SQL, MS Excel, and a range of industry-standard tools, and I bring a strong analytical mindset from my engineering background. I am passionate about using structured data to drive better decision-making and am eager to contribute to projects that require precision, consistency, and critical thinking in data labeling and annotation.

Labeling Experience

Data Annotation & Quality Review

MindriftTextEvaluation Rating

As a data annotator and quality reviewer at Mindrift, I assessed AI-generated content for relevance, accuracy, safety, and alignment with defined standards. My work involved providing feedback on AI outputs by comparing, rating, and flagging them according to evaluation criteria. This ensured the continuous improvement of AI models and maintained high-quality deliverables. • Evaluated text-based outputs for appropriateness and usefulness • Flagged quality issues and contributed to structured review processes • Ensured conformance to safety and guideline standards • Delivered detailed feedback supporting AI content quality

As a data annotator and quality reviewer at Mindrift, I assessed AI-generated content for relevance, accuracy, safety, and alignment with defined standards. My work involved providing feedback on AI outputs by comparing, rating, and flagging them according to evaluation criteria. This ensured the continuous improvement of AI models and maintained high-quality deliverables. • Evaluated text-based outputs for appropriateness and usefulness • Flagged quality issues and contributed to structured review processes • Ensured conformance to safety and guideline standards • Delivered detailed feedback supporting AI content quality

2023 - 2025

Education

Work History