David Otieno

Key Skills

Software

CloudFactory

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Entity Ner Classification

Segmentation

Freelancer Overview

I am a Computer Science and Data Science graduate with hands-on experience in data labeling, annotation review, and quality assurance for AI projects, particularly in the medical domain. My background includes validating both structured and unstructured datasets, refining annotation standards, and supporting domain experts to ensure high-quality, compliant data for diagnostic model training. I am skilled in Python (Pandas, NumPy), SQL, and data preprocessing, and have worked extensively with annotation workflows and quality assurance processes. My strong analytical reasoning, attention to detail, and ability to collaborate across teams help me consistently deliver accurate and reliable training data that supports improved AI model performance.

Labeling Experience

Annotation

CloudfactoryImageBounding BoxEntity Ner Classification

The annotation project involved labeling and validating structured and unstructured data to support machine learning model development, including tasks such as text classification, tagging, named entity recognition, sentiment analysis, image bounding box annotation, segmentation, and verification of pre-labeled datasets. The project handled large-scale datasets ranging from thousands to hundreds of thousands of data points, delivered in defined batches within strict timelines. Quality measures adhered to included comprehensive annotation guidelines, inter-annotator agreement checks, peer reviews, multi-level quality audits, documented edge cases, and continuous feedback loops, consistently maintaining accuracy thresholds of 95% and above to ensure reliability and model performance.

The annotation project involved labeling and validating structured and unstructured data to support machine learning model development, including tasks such as text classification, tagging, named entity recognition, sentiment analysis, image bounding box annotation, segmentation, and verification of pre-labeled datasets. The project handled large-scale datasets ranging from thousands to hundreds of thousands of data points, delivered in defined batches within strict timelines. Quality measures adhered to included comprehensive annotation guidelines, inter-annotator agreement checks, peer reviews, multi-level quality audits, documented edge cases, and continuous feedback loops, consistently maintaining accuracy thresholds of 95% and above to ensure reliability and model performance.

2025

Education

Work History