Bianca Scott

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Label Types

Bounding Box

Point Key Point

Segmentation

Classification

Tracking

Freelancer Overview

I am an AI Training Specialist with 3 years of hands-on experience in data labeling and annotation for machine learning and computer vision projects. My expertise includes annotating image, video, and audio datasets using tools like Labelbox, CVAT, Supervisely, VIA, and Roboflow, with a strong focus on accuracy and consistency. I am proficient in applying bounding boxes, polygon segmentation, and keypoint annotations, as well as preparing datasets for YOLO object detection models and conducting rigorous quality control audits. I thrive in remote, collaborative environments, supporting AI model development through structured data preparation, workflow tracking, and data validation. My background in IT and my attention to detail ensure that I deliver high-quality, reliable training data for advanced AI applications.

Labeling Experience

Video Annotation & Object Tracking for AI Training

LabelboxVideoBounding BoxPoint Key Point

Worked on multi-frame video annotation projects for computer vision and object detection systems. Performed frame-by-frame labeling and multi-object tracking to maintain object identity consistency across dynamic sequences. Applied bounding boxes and polygon segmentation to vehicles, pedestrians, and other objects in video datasets. Prepared datasets optimized for YOLO object detection models and real-time AI training pipelines. Conducted quality assurance checks and dataset validation to ensure high annotation accuracy and consistency. Collaborated in remote AI training environments to support dataset preparation and model performance improvement through structured and high-quality labeled data.

Worked on multi-frame video annotation projects for computer vision and object detection systems. Performed frame-by-frame labeling and multi-object tracking to maintain object identity consistency across dynamic sequences. Applied bounding boxes and polygon segmentation to vehicles, pedestrians, and other objects in video datasets. Prepared datasets optimized for YOLO object detection models and real-time AI training pipelines. Conducted quality assurance checks and dataset validation to ensure high annotation accuracy and consistency. Collaborated in remote AI training environments to support dataset preparation and model performance improvement through structured and high-quality labeled data.

2024

AI Training & Computer Vision Annotation Specialist

LabelboxImageBounding BoxPoint Key Point

As an AI Training Specialist with 3 years of experience, I have worked on multi-modal AI training datasets including image, video, audio, and text data. My responsibilities included annotating bounding boxes, polygon segmentation, and keypoint labeling for computer vision models, as well as frame-by-frame video tracking to maintain object identity consistency. I prepared and validated datasets for YOLO-based object detection systems and conducted detailed quality assurance reviews to ensure annotation accuracy above 97%. In NLP-focused tasks, I performed Named Entity Recognition (NER), transcription, text classification, and supervised fine-tuning (SFT) prompt-response evaluation. I also contributed to AI model evaluation and rating workflows to improve output reliability and alignment. All projects adhered to strict annotation guidelines, dataset version control processes, and confidentiality standards. I consistently met deadlines while maintaining high-quality data standards.

As an AI Training Specialist with 3 years of experience, I have worked on multi-modal AI training datasets including image, video, audio, and text data. My responsibilities included annotating bounding boxes, polygon segmentation, and keypoint labeling for computer vision models, as well as frame-by-frame video tracking to maintain object identity consistency. I prepared and validated datasets for YOLO-based object detection systems and conducted detailed quality assurance reviews to ensure annotation accuracy above 97%. In NLP-focused tasks, I performed Named Entity Recognition (NER), transcription, text classification, and supervised fine-tuning (SFT) prompt-response evaluation. I also contributed to AI model evaluation and rating workflows to improve output reliability and alignment. All projects adhered to strict annotation guidelines, dataset version control processes, and confidentiality standards. I consistently met deadlines while maintaining high-quality data standards.

2022 - 2024

Education

Work History