Jeremiah Githua

Key Skills

Software

Appen

Clickworker

iMerit

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Image

Text

Top Label Types

Audio Recording

Bounding Box

Classification

Freelancer Overview

I am a detail-oriented professional with a strong background in remote technical support, quality assurance, and structured data review, now focused on AI training data and data labeling roles. My experience includes text annotation, sentiment labeling, content moderation, and AI response evaluation, where I consistently follow detailed guidelines to ensure high-quality, accurate outputs. I am skilled in using tools such as Google Sheets, Microsoft Excel, and online dashboards, and I excel at identifying errors, classifying data, and maintaining consistency even in high-volume, repetitive workflows. My ability to work independently, deliver under deadlines, and provide clear documentation makes me well-suited for data annotation, LLM response evaluation, and quality analysis in AI projects.

Labeling Experience

AI Data Labeler / Annotator (Freelance, Remote)

AppenTextClassification

Engaged in remote projects involving text annotation, classification, and sentiment labeling following strict annotation guidelines. Ensured quality and consistency under repetitive workflows while performing tagging, data validation, and quality assurance for AI training. Evaluated AI response helpfulness, tone, and accuracy through structured evaluations and policy-based review. • Adhered to detailed task rubrics and annotation rules • Reviewed and classified textual data for clarity, relevance, and sentiment • Conducted error detection and quality checks for labeled outputs • Utilized online dashboards, spreadsheets, and documentation tools for workflow tracking.

Engaged in remote projects involving text annotation, classification, and sentiment labeling following strict annotation guidelines. Ensured quality and consistency under repetitive workflows while performing tagging, data validation, and quality assurance for AI training. Evaluated AI response helpfulness, tone, and accuracy through structured evaluations and policy-based review. • Adhered to detailed task rubrics and annotation rules • Reviewed and classified textual data for clarity, relevance, and sentiment • Conducted error detection and quality checks for labeled outputs • Utilized online dashboards, spreadsheets, and documentation tools for workflow tracking.

2019

Education

Work History