Jamie Edwards

Key Skills

Software

Data Annotation Tech

Labelbox

Lionbridge

Mercor

Mindrift

OpenCV AI Kit (OAK)

Remotasks

Scale AI

Telus

Trilldata Technologies

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Image

Text

Video

Top Label Types

Evaluation Rating

Red Teaming

Computer Programming Coding

Prompt Response Writing SFT

Transcription

Freelancer Overview

I am a Master of Public Health candidate with hands-on experience in data collection, annotation, and analysis across global health and clinical research projects. My work has involved designing and leading mixed-methods studies, managing both quantitative survey data and qualitative focus group transcripts, and ensuring data integrity for research in reproductive and maternal health. I have supported large-scale projects by interpreting and logging intervention data, standardizing data collection approaches, and collaborating with multidisciplinary teams to ensure high-quality, actionable datasets. My analytical skills are complemented by proficiency in Excel and a strong attention to detail, making me well-equipped for roles in data labeling, annotation, and AI training data, particularly in healthcare and social sciences domains.

Labeling Experience

AI Data Labeling & Model Evaluation Contributor (Remote)

Data Annotation TechImageEvaluation RatingRed Teaming

Reviewed image-based and text-based QA datasets for accuracy and consistency and verified AI-generated outputs against structured labeling criteria. Produced clear, standalone corrected answers when model responses were inaccurate and documented concise rationales for non-answerable items. Maintained a 7–8 minute per-item review pace while meeting strict quality benchmarks. • Applied adversarial evaluation techniques to identify model reasoning weaknesses • Ensured data integrity and applied rigorous quality control standards • Evaluated and corrected mislabeled outputs • Enforced policy and guideline compliance

Reviewed image-based and text-based QA datasets for accuracy and consistency and verified AI-generated outputs against structured labeling criteria. Produced clear, standalone corrected answers when model responses were inaccurate and documented concise rationales for non-answerable items. Maintained a 7–8 minute per-item review pace while meeting strict quality benchmarks. • Applied adversarial evaluation techniques to identify model reasoning weaknesses • Ensured data integrity and applied rigorous quality control standards • Evaluated and corrected mislabeled outputs • Enforced policy and guideline compliance

2022

Education

Work History