John Ehiabhili

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Text Summarization

Freelancer Overview

I am an experienced researcher and educator with a strong background in data analysis, artificial intelligence, and biomedical engineering. My work focuses on leveraging AI and advanced data analytics to optimize optical biosensors for healthcare diagnostics, which has given me hands-on experience with data labeling and annotation in the context of medical data. I am skilled in Python, MATLAB, and various data analysis tools, and have a proven track record of designing experiments, refining datasets, and ensuring high-quality training data for machine learning applications. My academic and professional journey has equipped me with a keen attention to detail, strong problem-solving abilities, and effective communication skills, making me well-suited for roles in AI training data and data annotation.

Labeling Experience

AI trainer (Contributor and Reviewer)

Scale AITextText Summarization

Project Scope and Tasks This project involved creating a text summarization, open and closed QA dataset to train Large Language Models (LLMs). Using Remotasks (Outlier) software, the core task was to write accurate, abstractive summaries for source texts related to LLM subject matter. Each summary needed to concisely capture the essential information from the provided articles and reports. Project Size and Execution The project comprised roughly 50,000 text-summary pairs. Labelers processed source texts averaging 500-800 words in length, adhering strictly to defined style and length guidelines to ensure dataset consistency and utility for model training. Quality Assurance Quality was maintained through a multi-tiered review and feedback system and measured by summary accuracy and fidelity to the source. Key metrics included inter-rater reliability checks and performance tracking via the Remotasks (Outlier) dashboard, focusing on guideline adherence and audit pass rates.

Project Scope and Tasks This project involved creating a text summarization, open and closed QA dataset to train Large Language Models (LLMs). Using Remotasks (Outlier) software, the core task was to write accurate, abstractive summaries for source texts related to LLM subject matter. Each summary needed to concisely capture the essential information from the provided articles and reports. Project Size and Execution The project comprised roughly 50,000 text-summary pairs. Labelers processed source texts averaging 500-800 words in length, adhering strictly to defined style and length guidelines to ensure dataset consistency and utility for model training. Quality Assurance Quality was maintained through a multi-tiered review and feedback system and measured by summary accuracy and fidelity to the source. Key metrics included inter-rater reliability checks and performance tracking via the Remotasks (Outlier) dashboard, focusing on guideline adherence and audit pass rates.

2024 - 2025

Education

Work History