Nicholas Njuguna

Key Skills

Software

AWS SageMaker

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Video

Top Label Types

Audio Recording

Bounding Box

Classification

Freelancer Overview

I am a detail-oriented and adaptable professional with a strong background in education, classroom management, and technology integration, which has equipped me with excellent skills for data labeling and AI training data roles. My experience developing lesson plans, tailoring content for diverse needs, and providing precise feedback has honed my attention to detail and ability to follow complex guidelines—key traits for high-quality data annotation. I am proficient in digital tools such as Google Classroom and Microsoft Teams, and I quickly learn new platforms and workflows. My strong communication skills and cultural awareness enable me to work effectively in diverse teams and understand nuanced language data, making me well-suited for projects in natural language processing and content moderation. I am committed to continuous growth and delivering accurate, reliable results in fast-paced environments.

Labeling Experience

Data Annotator

Aws SagemakerImageBounding Box

Worked as a Data Labeler on an EdTech-focused computer vision project, using AWS SageMaker Ground Truth to annotate image datasets. Responsibilities included drawing accurate bounding boxes around objects of interest according to detailed labeling guidelines. Ensured high-quality annotations through consistent adherence to quality standards, regular self-review, and correction of feedback from quality assurance teams. Contributed to building reliable training data for machine learning models by maintaining accuracy, consistency, and completeness across large volumes of image data within project timelines.

Worked as a Data Labeler on an EdTech-focused computer vision project, using AWS SageMaker Ground Truth to annotate image datasets. Responsibilities included drawing accurate bounding boxes around objects of interest according to detailed labeling guidelines. Ensured high-quality annotations through consistent adherence to quality standards, regular self-review, and correction of feedback from quality assurance teams. Contributed to building reliable training data for machine learning models by maintaining accuracy, consistency, and completeness across large volumes of image data within project timelines.

2024 - 2025

Data Labeller

AppenVideoBounding Box

The project involves data annotating in the EdTech field.

The project involves data annotating in the EdTech field.

2023 - 2025

Education

Work History