Daniel Mumo

Key Skills

Software

Appen

Argilla

Axiom AI

Clickworker

CloudFactory

CrowdSource

Mindrift

Scale AI

SuperAnnotate

Telus

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Audio

Computer Code Programming

Top Task Types

Action Recognition

Audio Recording

Classification

Computer Programming Coding

Freelancer Overview

As an experienced AI Data Annotation and NLP Specialist with over 11 years of professional experience, I have contributed to diverse projects involving text, image, and linguistic data labeling for AI model development. My expertise includes natural language processing (NLP), LLM evaluation, and data quality assurance for multilingual datasets in English and Swahili. I have worked extensively on model evaluation, content classification, sentiment analysis, and entity recognition, collaborating closely with data scientists to improve AI accuracy and performance. My attention to detail, ability to follow complex annotation guidelines, and consistency under tight deadlines set me apart. I am also proficient with industry tools such as Label Studio, Prodigy, and Amazon SageMaker, ensuring high-quality, structured datasets ready for model training.

Labeling Experience

LLM Evaluation and Text Annotation Project

MindriftTextEntity Ner Classification

Contributed to a multilingual LLM evaluation and text annotation project focused on improving AI understanding and response quality in English and Swahili. Tasks included entity recognition, content classification, text summarization, and rating AI-generated outputs for coherence, accuracy, and tone. Ensured consistent labeling quality across large datasets through detailed review and adherence to strict annotation guidelines. Collaborated with global teams to refine evaluation protocols and support fine-tuning of large-scale models.

Contributed to a multilingual LLM evaluation and text annotation project focused on improving AI understanding and response quality in English and Swahili. Tasks included entity recognition, content classification, text summarization, and rating AI-generated outputs for coherence, accuracy, and tone. Ensured consistent labeling quality across large datasets through detailed review and adherence to strict annotation guidelines. Collaborated with global teams to refine evaluation protocols and support fine-tuning of large-scale models.

2013 - 2023

Education

Work History