Moses Onkoba

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Text Generation

Mapping

Evaluation Rating

Freelancer Overview

I am an analytical and detail-oriented Applied Mathematics student with hands-on experience in data analysis, content evaluation, and information verification. My background includes working on academic research projects where I analyzed quantitative datasets, identified patterns, and ensured the logical consistency of data. I am skilled in data labeling, annotation, and quality assurance, with a strong ability to follow structured guidelines and detect errors or inconsistencies. I am proficient in using tools like Microsoft Excel, Google Sheets, and various AI platforms, and I excel at organizing and categorizing information for machine learning and AI training purposes. My strong attention to detail, logical reasoning, and ability to work independently make me well-suited for roles in AI data annotation, model evaluation, and prompt review.

Labeling Experience

Data Review and Annotation Practice - Independent Projects

AppenTextText GenerationMapping

Performed evaluation and classification of written and numerical data for logical accuracy and consistency as part of independent data labeling projects. Categorized information according to structured guidelines to support AI and machine learning model development. Reviewed AI-generated text outputs, identifying errors and inconsistencies, and maintained thorough documentation of results. • Evaluated text and numerical datasets for label accuracy • Categorized data according to predefined classification schemes • Reviewed and critiqued AI-generated responses • Documented evaluation findings for quality control

Performed evaluation and classification of written and numerical data for logical accuracy and consistency as part of independent data labeling projects. Categorized information according to structured guidelines to support AI and machine learning model development. Reviewed AI-generated text outputs, identifying errors and inconsistencies, and maintained thorough documentation of results. • Evaluated text and numerical datasets for label accuracy • Categorized data according to predefined classification schemes • Reviewed and critiqued AI-generated responses • Documented evaluation findings for quality control

2025

Education

Work History