Stella Ndirangu

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Video

Top Label Types

Bounding Box

Segmentation

Freelancer Overview

I am a detail-oriented Data Annotation Specialist with over four years of experience supporting computer vision and machine learning projects through high-precision data labeling and quality assurance. My expertise covers annotating complex datasets—including images, videos, and structured data—using tools such as CVAT, Remotasks, CrowdFactory, and Atlas Capture. I have a strong background in computer science and am skilled in applying bounding boxes, segmentation, and object detection techniques to create reliable AI training datasets. I consistently deliver high-accuracy results by adhering to strict annotation guidelines and collaborating with distributed AI teams. My experience spans large-scale labeling projects, dataset validation, and workflow optimization, allowing me to contribute to improved model training performance while meeting tight deadlines in remote work environments.

Labeling Experience

Data Annotation Specialist

Data Annotation TechVideoBounding BoxSegmentation

• Annotated thousands of image and video datasets used to train computer vision models for object detection, classification, and segmentation tasks. • Maintained annotation accuracy and consistency by strictly following project-specific guidelines and quality standards. • Completed high-volume labeling tasks within tight deadlines while maintaining high precision rates. • Identified annotation inconsistencies and corrected errors to improve dataset reliability for machine learning training. • Collaborated with distributed teams and project managers to resolve annotation ambiguities and ensure guideline compliance. • Contributed to quality control processes by reviewing datasets and ensuring annotation completeness.

• Annotated thousands of image and video datasets used to train computer vision models for object detection, classification, and segmentation tasks. • Maintained annotation accuracy and consistency by strictly following project-specific guidelines and quality standards. • Completed high-volume labeling tasks within tight deadlines while maintaining high precision rates. • Identified annotation inconsistencies and corrected errors to improve dataset reliability for machine learning training. • Collaborated with distributed teams and project managers to resolve annotation ambiguities and ensure guideline compliance. • Contributed to quality control processes by reviewing datasets and ensuring annotation completeness.

2020

Education

Work History