David Edison

Key Skills

Software

Clickworker

Remotasks

Scale AI

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Action Recognition

Audio Recording

Computer Programming Coding

Data Collection

Text Generation

Freelancer Overview

As an enthusiastic and highly detail-oriented professional, I am eager to begin my journey in AI training and data labeling. While I am new to formal data annotation projects, my background has equipped me with strong analytical skills, exceptional attention to detail, and a meticulous approach to following complex instructions. I am proficient in accurately understanding and categorizing various types of data. I am a quick and versatile learner, highly adaptable to new tools and guidelines, and committed to producing high-quality work to support the development of cutting-edge AI systems. I am excited to apply my dedication and precision to real-world data labeling tasks and contribute effectively to diverse AI projects on OpenTrain

Labeling Experience

Data Annotation Quality Analyst

AppenVideoQuestion AnsweringEmotion Recognition

Worked on detailed annotation and moderation of video datasets used to train AI models for computer vision tasks like object detection, human action recognition, and multi-object tracking. Responsibilities included accurately identifying, labeling, and tracking objects across frames using bounding boxes, polygons, and key points, while maintaining consistency and quality across long video sequences. Also involved reviewing flagged content for appropriateness and ensuring adherence to client guidelines for data privacy and ethical standards. Collaborated with cross-functional teams to validate annotations, provided feedback to improve annotation guidelines, and participated in quality assurance rounds to guarantee dataset integrity. These projects contributed to enhancing AI capabilities in real-time video analysis for applications such as traffic monitoring, content moderation, and autonomous navigation.

Worked on detailed annotation and moderation of video datasets used to train AI models for computer vision tasks like object detection, human action recognition, and multi-object tracking. Responsibilities included accurately identifying, labeling, and tracking objects across frames using bounding boxes, polygons, and key points, while maintaining consistency and quality across long video sequences. Also involved reviewing flagged content for appropriateness and ensuring adherence to client guidelines for data privacy and ethical standards. Collaborated with cross-functional teams to validate annotations, provided feedback to improve annotation guidelines, and participated in quality assurance rounds to guarantee dataset integrity. These projects contributed to enhancing AI capabilities in real-time video analysis for applications such as traffic monitoring, content moderation, and autonomous navigation.

2021 - 2025

AI Data Moderator

Scale AIImageSegmentationClassification

As an AI Data Moderator specializing in image annotation, I have meticulously labeled, reviewed, and moderated large sets of image data to ensure high-quality, accurate training datasets for machine learning models. My role involved identifying and tagging objects, people, scenes, and inappropriate or sensitive content, applying techniques such as bounding boxes, polygon segmentation, and classification to provide contextual information critical for computer vision applications. I ensured all annotations met project guidelines and ethical standards, including detecting subtle image manipulations and content that requires cultural sensitivity. Additionally, I collaborated with AI teams to validate model outputs, perform quality control, and improve dataset consistency across diverse industries such as social media, e-commerce, and autonomous systems. This work advanced the precision and reliability of AI-powered image recognition and content moderation systems.

As an AI Data Moderator specializing in image annotation, I have meticulously labeled, reviewed, and moderated large sets of image data to ensure high-quality, accurate training datasets for machine learning models. My role involved identifying and tagging objects, people, scenes, and inappropriate or sensitive content, applying techniques such as bounding boxes, polygon segmentation, and classification to provide contextual information critical for computer vision applications. I ensured all annotations met project guidelines and ethical standards, including detecting subtle image manipulations and content that requires cultural sensitivity. Additionally, I collaborated with AI teams to validate model outputs, perform quality control, and improve dataset consistency across diverse industries such as social media, e-commerce, and autonomous systems. This work advanced the precision and reliability of AI-powered image recognition and content moderation systems.

2020 - 2025

Data Annotation

ClickworkerTextEntity Ner ClassificationText Generation

From 2019 to 2025, participated extensively in diverse data labeling and annotation projects across multiple crowdsourcing platforms. Tasks included classifying large volumes of text for sentiment and topic analysis, performing entity recognition on e-commerce and social media data, audio transcription and emotion tagging for customer support datasets, and evaluating AI-generated responses. Collaborated with international teams to ensure consistent and high-quality annotation standards, following detailed project guidelines and leveraging platform-specific annotation tools. Start Date: March 2019 End Date: September 2025 On-going Project: No

From 2019 to 2025, participated extensively in diverse data labeling and annotation projects across multiple crowdsourcing platforms. Tasks included classifying large volumes of text for sentiment and topic analysis, performing entity recognition on e-commerce and social media data, audio transcription and emotion tagging for customer support datasets, and evaluating AI-generated responses. Collaborated with international teams to ensure consistent and high-quality annotation standards, following detailed project guidelines and leveraging platform-specific annotation tools. Start Date: March 2019 End Date: September 2025 On-going Project: No

2019 - 2025

Education

Work History