Ruth J.

Key Skills

Software

Labelbox

Other

Appen

Top Subject Matter

Autonomous vehicles

Computer Vision

Facial recognition

Top Data Types

Image

Text

Top Task Types

Segmentation

Point/Key Point

Emotion Recognition

Freelancer Overview

Data Annotation Specialist. Core strengths include Labelbox, Other, and Appen. Education includes Bachelor of Science, Covent University (2025). AI-training focus includes data types such as Image and Text and labeling workflows including Segmentation, Point, and Key Point.

Labeling Experience

Data Annotation Specialist

LabelboxImageSegmentation

As a Data Annotation Specialist at SAMA, I performed high-volume image segmentation and object detection labeling for autonomous vehicle training datasets. I ensured a consistent 98% quality score across weekly audits and collaborated to refine annotation guidelines. I actively contributed to identifying systematic errors, improving workflow efficiency for our annotation team. • Maintained accuracy above 98% for all assigned tasks. • Focused on labeling datasets critical for computer vision and self-driving technology. • Used advanced features in both Labelbox and CVAT for efficient annotation. • Helped update guidelines, reducing edge-case ambiguity by 15%.

2025 - Present

NLP Sentiment Dataset Annotator

AppenTextEmotion Recognition

For an NLP Sentiment Dataset project, I labeled over 2,000 social media comments to detect emotional tone and sarcasm. My labels were used to enhance a customer service chatbot's understanding of conversational sentiment. Precision and consistency were prioritized to improve the model's reliability. • Performed detailed emotion and sarcasm annotation on text data. • Supported NLP model refinement for customer support purposes. • Ensured high-quality labels for difficult or ambiguous language cases. • Delivered the processed dataset on schedule.

Not specified

Open Source Image Tagging Project Annotator

OtherImagePoint/Key Point

In an Open Source Image Tagging Project, I annotated over 5,000 facial images using Keypoint Annotation techniques for a recognition dataset. The project required balancing the dataset for demographic diversity and algorithmic fairness. I worked independently to ensure labeling accuracy and data representativeness. • Utilized advanced annotation tools for keypoint capture. • Focused on facial recognition training to reduce bias. • Adhered to strict guidelines for data diversification. • Delivered the entire dataset within the project timeline.

Not specified

Education