Stephanie Trafford

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Medical Dicom

Top Task Types

Action Recognition

Audio Recording

Classification

Computer Programming Coding

Evaluation Rating

Freelancer Overview

As an experienced AI Data Annotator with a strong foundation in Computer Science, I bring over five years of hands-on expertise in high-quality data labeling across text, image, and video formats. At Outlier.ai and Aligner.ai, I consistently delivered precise annotations using tools like Labelbox, collaborated with Data Science teams to optimize machine learning models, and wrote automation scripts in Python to streamline workflows boosting productivity and reducing error rates. My ability to manage large datasets with SQL and apply best practices in annotation helped support the development of high-performing AI systems in NLP and computer vision domains. What sets me apart is a proven commitment to data integrity, technical fluency in Python, SQL, and OpenCV, and a passion for advancing annotation standards. I’ve authored internal resources on labeling techniques, contributed to cross-functional research projects, and shared my insights at industry events such as the AI Data Summit. Certified as both a Machine Learning Associate and Data Annotation Specialist, I thrive in remote, collaborative environments and aim to ensure top-tier data quality in every project.

Labeling Experience

Multimodal Annotation for AI Models

LabelboxVideoBounding BoxPolygon

Annotated large-scale datasets involving images, videos, and textual content to support NLP and computer vision models. Tasks included bounding box labeling, text classification, and named entity recognition. Contributed to reducing model error rates by 20% and maintained 98% annotation accuracy. Developed Python scripts to automate repetitive labeling tasks, improving efficiency and saving 15+ hours weekly.

Annotated large-scale datasets involving images, videos, and textual content to support NLP and computer vision models. Tasks included bounding box labeling, text classification, and named entity recognition. Contributed to reducing model error rates by 20% and maintained 98% annotation accuracy. Developed Python scripts to automate repetitive labeling tasks, improving efficiency and saving 15+ hours weekly.

2022

Education

Work History