Isaac Juma

Key Skills

Software

Labelbox

Other

Top Subject Matter

Kenya National Corpus

historical texts

Kenya literature

Top Data Types

Text

Audio

Image

Document

Top Task Types

Entity (NER) Classification

Audio Recording

Object Detection

Classification

Freelancer Overview

Research Data Specialist & Academic Operations Lead. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox. Education includes Master of Education, University of Nairobi (2023) and Bachelor of Science, University of Nairobi (2022). AI-training focus includes data types such as Text and labeling workflows including Classification.

Labeling Experience

Research Data Specialist & Academic Operations Lead

LabelboxTextClassification

As the Research Data Specialist & Academic Operations Lead, I engineered structured data collection frameworks and applied sophisticated classification and taxonomy systems to academic datasets. I specialized in transforming unstructured research data into organized, machine-readable forms for use in natural language processing and predictive AI models. My role demanded precise adherence to annotation guidelines, multi-stage quality control, and large-scale text annotation for research and enterprise AI projects. • Designed and executed multi-format annotation protocols for text-based research records. • Led the systematic categorization and semantic tagging of thousands of academic documents. • Applied inter-annotator agreement and multi-tier validation procedures to maintain data integrity. • Utilized advanced Excel-based and enterprise data annotation software including Labelbox, Scale AI, Appen, Remotasks, and internal proprietary tools.

As the Research Data Specialist & Academic Operations Lead, I engineered structured data collection frameworks and applied sophisticated classification and taxonomy systems to academic datasets. I specialized in transforming unstructured research data into organized, machine-readable forms for use in natural language processing and predictive AI models. My role demanded precise adherence to annotation guidelines, multi-stage quality control, and large-scale text annotation for research and enterprise AI projects. • Designed and executed multi-format annotation protocols for text-based research records. • Led the systematic categorization and semantic tagging of thousands of academic documents. • Applied inter-annotator agreement and multi-tier validation procedures to maintain data integrity. • Utilized advanced Excel-based and enterprise data annotation software including Labelbox, Scale AI, Appen, Remotasks, and internal proprietary tools.

2022 - Present

Data Annotation

ImageBounding Box

Worked as a Data Annotation Specialist and Research Data Specialist with over four years of experience in structured data labeling, AI training dataset preparation, and quality assurance. Applied annotation expertise across classification, taxonomy design, semantic tagging, entity recognition, sentiment analysis, and dataset preparation for AI systems. Built and maintained structured data collection frameworks, performed large-scale categorization and validation, and ensured high accuracy through multi-stage quality control workflows. Experienced in handling high-volume annotation tasks, resolving edge cases, improving guideline adherence, and preparing machine-readable datasets for NLP, computer vision, and predictive AI applications. Maintained sub-2% error rates, developed validation systems to improve consistency, and supported research and academic projects requiring strict compliance, confidentiality, and precision.

Worked as a Data Annotation Specialist and Research Data Specialist with over four years of experience in structured data labeling, AI training dataset preparation, and quality assurance. Applied annotation expertise across classification, taxonomy design, semantic tagging, entity recognition, sentiment analysis, and dataset preparation for AI systems. Built and maintained structured data collection frameworks, performed large-scale categorization and validation, and ensured high accuracy through multi-stage quality control workflows. Experienced in handling high-volume annotation tasks, resolving edge cases, improving guideline adherence, and preparing machine-readable datasets for NLP, computer vision, and predictive AI applications. Maintained sub-2% error rates, developed validation systems to improve consistency, and supported research and academic projects requiring strict compliance, confidentiality, and precision.

2022 - 2025

Education

Work History