Chikamdabere Nzeka

Key Skills

Software

Labelbox

Top Subject Matter

Pharmaceutical Science

Medical Domain Expertise

Healthcare Data & Medical Documentation

Top Data Types

Text

Image

Document

Top Task Types

RLHF

Freelancer Overview

Research & Data Projects – Data Annotation and Evaluation. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox. Education includes Master of Science, University of Brighton (2025) and Bachelor of Science, University of Brighton (2021). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and RLHF.

Labeling Experience

Freelance Academic Writing & AI Content Quality Review

LabelboxTextRLHF

Evaluated AI-generated health content against pharmaceutical science knowledge, identifying unsafe or misleading information with clear rationale. Edited and improved technical and medical documents for accuracy, logical consistency, and risk explanation. Wrote alt-text and structured image descriptions, enhancing accessibility and usability for AI training datasets. • Flagged unsafe advice and provided justification • Reviewed model outputs for alignment with medical standards • Created high-quality text descriptions for AI image training • Ensured clarity and factual accuracy in revised documents

Evaluated AI-generated health content against pharmaceutical science knowledge, identifying unsafe or misleading information with clear rationale. Edited and improved technical and medical documents for accuracy, logical consistency, and risk explanation. Wrote alt-text and structured image descriptions, enhancing accessibility and usability for AI training datasets. • Flagged unsafe advice and provided justification • Reviewed model outputs for alignment with medical standards • Created high-quality text descriptions for AI image training • Ensured clarity and factual accuracy in revised documents

2022 - Present

Research & Data Projects – Data Annotation and Evaluation

LabelboxText

Reviewed and evaluated over 300 clinical research papers and patient datasets for factual accuracy and regulatory compliance, simulating the process of assessing AI model outputs. Organized and labeled complex pharmaceutical datasets in Excel, ensuring above 99% annotation accuracy for thousands of data points. Conducted peer review of medical documents, focusing on error detection and justifications akin to AI content and model safety evaluation. • Maintained high annotation accuracy in structured datasets • Generated detailed reports summarizing findings • Applied statistical validation and anomaly detection to labeled data • Precisely reviewed content for both human and AI application purposes

Reviewed and evaluated over 300 clinical research papers and patient datasets for factual accuracy and regulatory compliance, simulating the process of assessing AI model outputs. Organized and labeled complex pharmaceutical datasets in Excel, ensuring above 99% annotation accuracy for thousands of data points. Conducted peer review of medical documents, focusing on error detection and justifications akin to AI content and model safety evaluation. • Maintained high annotation accuracy in structured datasets • Generated detailed reports summarizing findings • Applied statistical validation and anomaly detection to labeled data • Precisely reviewed content for both human and AI application purposes

2021 - 2025

Education

Work History