Philis Mwelu

Key Skills

Software

OneForma

Appen

Top Subject Matter

Scientific Domain Expertise

Medical Domain Expertise

General Knowledge

Top Data Types

Text

Document

Image

Top Task Types

Point Key Point

Classification

Object Detection

Text Generation

Question Answering

Text Summarization

Transcription

Data Collection

Freelancer Overview

I have experience in data labeling and AI training from working with Appen and OneForma, where I performed content evaluation, data annotation, and AI response rating while following detailed guidelines and maintaining high accuracy. My background in biochemistry and freelance research writing strengthens my analytical skills, attention to detail, and ability to identify inconsistencies and edge cases, enabling me to deliver consistent, high-quality annotations in fast-paced environments.

Labeling Experience

Freelance AI Data Annotator & Content Evaluator

OneformaText

As a freelance AI data annotator and content evaluator, I performed rigorous annotation and evaluation tasks for large language model (LLM) training pipelines. My work included labeling, reviewing, and validating scientific, medical, and general-knowledge datasets, ensuring quality and accuracy for AI development. I supported both English and Swahili data annotation, aiding multilingual AI training efforts. • Conducted dataset annotation and validation, including scientific and medical content. • Evaluated AI model outputs for relevance, factual correctness, and safety. • Tagged linguistic features such as sentiment, intent, and named entities in scientific texts. • Used strict guidelines and detailed annotation rubrics for consistency and quality control.

As a freelance AI data annotator and content evaluator, I performed rigorous annotation and evaluation tasks for large language model (LLM) training pipelines. My work included labeling, reviewing, and validating scientific, medical, and general-knowledge datasets, ensuring quality and accuracy for AI development. I supported both English and Swahili data annotation, aiding multilingual AI training efforts. • Conducted dataset annotation and validation, including scientific and medical content. • Evaluated AI model outputs for relevance, factual correctness, and safety. • Tagged linguistic features such as sentiment, intent, and named entities in scientific texts. • Used strict guidelines and detailed annotation rubrics for consistency and quality control.

2023 - Present

Education

Work History