Kenneth Wambua

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Polygon

Segmentation

Object Detection

Tracking

Freelancer Overview

I bring over six years of experience in technical writing, data analysis, and AI content evaluation, with a strong foundation in mathematics, economics, and IT. My work spans academic, technical, and business domains, where I have evaluated and refined AI-generated outputs for clarity, accuracy, and audience engagement. I am skilled in data labeling principles, including RLHF, and have hands-on experience providing feedback to improve AI reasoning and reduce bias. My technical toolkit includes Excel, Python, and R for data analysis, as well as Canva and Adobe Suite for visual communication. I have contributed to projects such as developing an AI-assisted supply chain dashboard and conducting ethical assessments in industry, consistently applying critical thinking and attention to detail. My cross-disciplinary background enables me to adapt quickly and deliver high-quality, reliable data annotation and content evaluation for diverse AI training needs.

Labeling Experience

Data Annotator - Computer Vision & Image Labeling

Scale AIImageBounding BoxPolygon

Worked as a Data Annotator on computer vision projects focused on autonomous vehicle perception systems. Annotated large-scale image datasets by accurately labeling vehicles, pedestrians, cyclists, traffic signs, and road infrastructure using bounding boxes, polygons, segmentation masks, and cuboids. Followed strict annotation guidelines to ensure high inter-annotator agreement and data consistency. Conducted quality checks, corrected edge-case labels, and met tight turnaround timelines while maintaining high accuracy. Collaborated with QA reviewers and project leads to improve annotation precision, reduce error rates, and support model training for object detection and tracking tasks in real-world driving environments.

Worked as a Data Annotator on computer vision projects focused on autonomous vehicle perception systems. Annotated large-scale image datasets by accurately labeling vehicles, pedestrians, cyclists, traffic signs, and road infrastructure using bounding boxes, polygons, segmentation masks, and cuboids. Followed strict annotation guidelines to ensure high inter-annotator agreement and data consistency. Conducted quality checks, corrected edge-case labels, and met tight turnaround timelines while maintaining high accuracy. Collaborated with QA reviewers and project leads to improve annotation precision, reduce error rates, and support model training for object detection and tracking tasks in real-world driving environments.

2021 - 2022

Education

Work History