Elisha E.

Key Skills

Software

CVAT

Labelbox

Label Studio

Top Subject Matter

AI Training Data Specialist | Text & Image Annotation

Technology & Artificial Intelligence – Training Data Annotation

AI Data Annotator | Data Validation, Classification & QA

Top Data Types

Image

Text

Audio

Top Task Types

Classification

Computer Programming/Coding

Transcription

Text Generation

Question Answering

Freelancer Overview

Over the past few years, I have worked on tasks that required reviewing, organizing, and labeling different types of data to ensure accuracy and consistency. This includes evaluating text, categorizing information, identifying patterns, and following detailed guidelines to maintain high-quality datasets. Through this experience, I have developed a strong eye for detail, the ability to work efficiently with large volumes of information, and a commitment to producing reliable results that can be used to support AI and machine learning systems. My background in technology has also given me a deeper understanding of how data quality affects the performance of AI models. I am comfortable learning new tools and workflows, adapting to changing project requirements, and maintaining accuracy under tight deadlines. I take pride in being thorough, organized, and dependable, and I consistently focus on delivering work that meets both quality standards and project goals.

Labeling Experience

AI Training Data Quality Assurance and Validation

ImageEvaluation/Rating

Conducted quality reviews of previously annotated datasets to identify labeling errors, inconsistencies, and guideline violations. Responsibilities included validating annotations, correcting inaccurate labels, documenting issues, and providing feedback to improve overall dataset quality. Reviewed over 20,000 annotation records across text and image datasets while maintaining strict quality standards and project deadlines.

2025 - 2025

Semantic Segmentation for Urban Environment Analysis

ImageSegmentation

Performed pixel-level annotation of urban road scenes to support semantic segmentation model training. Objects such as vehicles, roads, pedestrians, buildings, and vegetation were carefully outlined and assigned class labels. The project required meticulous attention to detail and strict compliance with segmentation guidelines to ensure high-quality training data. Regular audits and validation procedures were used to maintain annotation consistency.

2024 - 2024

Road Scene Object Detection Annotation

ImageBounding Box

Annotated road scene images for object detection models by drawing precise bounding boxes around vehicles, buses, pedestrians, and other traffic-related objects. The project included reviewing thousands of urban traffic images and ensuring each object was accurately labeled according to annotation standards. Quality assurance procedures included peer review and spot-check validation to maintain high dataset accuracy.

2024 - 2024

Multi-Category Image Classification for Computer Vision Training

ImageClassification

Performed image classification on large datasets containing everyday objects such as animals, vehicles, aircraft, and household items. Images were reviewed and assigned to predefined categories to support machine learning model development. The project involved labeling more than 15,000 images while maintaining consistency across categories and complying with detailed annotation instructions. Quality checks were performed to verify label correctness and reduce classification errors.

2024 - 2024

Customer Support Ticket Classification and Tagging

TextText Generation

Annotated and classified customer support tickets to help train natural language processing (NLP) models. Tasks involved reviewing customer messages, identifying user intent, assigning appropriate categories, and applying metadata tags. The dataset contained over 10,000 customer interactions covering account access, password resets, billing inquiries, and technical support requests. Strict annotation guidelines were followed to maintain consistency, and regular quality assurance reviews were conducted to ensure annotation accuracy exceeded project requirements.

2024 - 2024

Education

Work History