Hudson Nyongesa

Key Skills

Software

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Polygon

Freelancer Overview

I have a strong background in data analysis, process improvement, and team management, built on my academic foundation in Mathematics and Computer Science. My experience managing global teams and streamlining workflows has given me a keen eye for detail and quality assurance, which is essential for data labeling and annotation projects. I have a proven track record of improving data accuracy and operational efficiency, having implemented measures that reduced error rates and enhanced consistency in project delivery. My roles have required me to interpret complex data, create clear documentation, and support compliance, all of which are valuable in preparing and validating high-quality AI training data. I am passionate about contributing to data-driven projects and am eager to apply my analytical skills and technical expertise to support the development of reliable AI systems.

Labeling Experience

Image Annotation for Object Detection and Classification

CVATImagePolygon

Worked on large-scale image annotation projects aimed at training and validating computer vision models. Responsibilities included annotating images using bounding boxes and polygons for object detection, as well as image-level classification tasks. Objects were labeled according to strict client-defined guidelines to ensure high inter-annotator agreement and model accuracy. Conducted quality assurance checks by reviewing peer annotations, correcting label inconsistencies, and flagging ambiguous cases for escalation. Maintained high accuracy while meeting productivity benchmarks. This work directly supported supervised learning pipelines for object recognition and scene understanding models.

Worked on large-scale image annotation projects aimed at training and validating computer vision models. Responsibilities included annotating images using bounding boxes and polygons for object detection, as well as image-level classification tasks. Objects were labeled according to strict client-defined guidelines to ensure high inter-annotator agreement and model accuracy. Conducted quality assurance checks by reviewing peer annotations, correcting label inconsistencies, and flagging ambiguous cases for escalation. Maintained high accuracy while meeting productivity benchmarks. This work directly supported supervised learning pipelines for object recognition and scene understanding models.

2025 - 2025

Education

Work History