Philip Njuguna

Key Skills

Software

Appen

Clickworker

Lionbridge

OneForma

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Video

Top Label Types

Entity Ner Classification

Mapping

Prompt Response Writing SFT

Relationship

Segmentation

Freelancer Overview

I have a strong background in information and communication technology with hands-on experience in IT support, system administration, and cybersecurity. My technical expertise includes working with Linux environments (Kali, Ubuntu, Tails), scripting in Python and Bash, and using tools for data analysis and automation. I am skilled in network configuration, digital forensics, and log analysis, which gives me a keen eye for detail—an essential quality for data labeling and annotation tasks. I have experience documenting systems, managing digital assets, and ensuring data integrity through regular audits and security practices. My continuous learning in areas like ethical hacking, penetration testing, and cybersecurity keeps me up-to-date with the latest technologies, enabling me to deliver accurate and high-quality training data for AI and machine learning projects.

Labeling Experience

data labeling

OneformaImageEntity Ner ClassificationSegmentation

I worked on a data labeling project focused on preparing high quality annotated datasets for machine learning models. The objective of the project was to transform raw, unstructured data into accurately labeled training data that could be used to improve model performance and reliability. The project involved labeling different types of data, including text and image data, depending on the task requirements. For text data, I performed classification, entity recognition, sentiment tagging, and content categorization based on predefined labeling guidelines. For image data, I carried out object identification and bounding box annotations, ensuring consistency and precision across all samples. I followed strict annotation guidelines to maintain label accuracy and reduce bias. Each task required careful attention to detail, cross checking labels, and validating edge cases where the data was ambiguous. I also participated in quality assurance processes, including reviewing peer annotations

I worked on a data labeling project focused on preparing high quality annotated datasets for machine learning models. The objective of the project was to transform raw, unstructured data into accurately labeled training data that could be used to improve model performance and reliability. The project involved labeling different types of data, including text and image data, depending on the task requirements. For text data, I performed classification, entity recognition, sentiment tagging, and content categorization based on predefined labeling guidelines. For image data, I carried out object identification and bounding box annotations, ensuring consistency and precision across all samples. I followed strict annotation guidelines to maintain label accuracy and reduce bias. Each task required careful attention to detail, cross checking labels, and validating edge cases where the data was ambiguous. I also participated in quality assurance processes, including reviewing peer annotations

2024

Education

Work History