Mcgriff Travis

Key Skills

Software

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Segmentation

Classification

Freelancer Overview

With over 7 years of experience in data analysis and quality assurance, I specialize in evaluating large datasets for accuracy, consistency, and completeness. My background includes reviewing and validating complex simulation data, developing structured rubrics and checklists, and identifying anomalies to ensure high-quality deliverables. I have worked closely with cross-functional teams to provide clear documentation and actionable feedback, enhancing data integrity for engineering and research projects. My skills include error detection, pattern recognition, and the use of data visualization tools, along with proficiency in Microsoft Office Suite, Google Workspace, and basic Python. I am passionate about contributing to AI training data and data labeling projects by leveraging my keen attention to detail and commitment to quality.

Labeling Experience

Medical Image Annotation for Disease Detection (Hospital Dataset)

CVATImageBounding BoxSegmentation

Worked on a healthcare-focused medical imaging annotation project involving X-ray and CT scan datasets for disease detection and diagnosis support. Responsibilities included labeling anatomical regions and abnormal findings using bounding boxes and pixel-level segmentation masks. Also performed image-level classification for conditions such as pneumonia, fractures, lung infection patterns, and tumor indicators. The project covered thousands of images and required strict compliance with labeling guidelines, consistency checks, and quality control processes. Maintained high accuracy by following annotation protocols, using review cycles, and resolving edge-case ambiguities through documented feedback.

Worked on a healthcare-focused medical imaging annotation project involving X-ray and CT scan datasets for disease detection and diagnosis support. Responsibilities included labeling anatomical regions and abnormal findings using bounding boxes and pixel-level segmentation masks. Also performed image-level classification for conditions such as pneumonia, fractures, lung infection patterns, and tumor indicators. The project covered thousands of images and required strict compliance with labeling guidelines, consistency checks, and quality control processes. Maintained high accuracy by following annotation protocols, using review cycles, and resolving edge-case ambiguities through documented feedback.

2023 - 2024

Education

Work History