Gad Abolo

Key Skills

Software

Appen

Clickworker

CVAT

Data Annotation Tech

Labelbox

LabelImg

Label Studio

Mercor

Mighty AI

Mindrift

OneForma

Scale AI

Other

Top Subject Matter

No subject matter listed

Top Data Types

Document

Image

Text

Video

Top Task Types

Action Recognition

Bounding Box

Classification

Data Collection

Entity Ner Classification

Object Detection

Tracking

Transcription

Freelancer Overview

I am a detail-oriented AI Data Annotation Specialist and Data Analyst with over 5 years of remote experience delivering high-quality, labeled datasets for AI model training. My expertise covers a wide range of annotation types—including bounding box, object detection, text classification, entity recognition (NER), sentiment analysis, action recognition, and video tracking—using industry-standard tools like LabelImg, Label Studio, and CVAT. I have successfully completed hundreds of projects for international clients in domains such as retail, e-commerce, customer analytics, and market research, consistently achieving top accuracy and reliability ratings. My strong background in data cleaning, Power BI, Excel, and SQL enables me to transform raw, unstructured data into precise, model-ready training data at scale. I am adept at adapting to new guidelines, maintaining strict quality standards, and excelling in deadline-driven remote environments.

Labeling Experience

Customer behavior tracking in retail store

Label StudioVideoBounding BoxClassification

Performed frame-by-frame video annotation on sample retail store footage, drawing bounding boxes around customers and staff, tracking movement paths across frames, and classifying actions such as browsing, product interaction, and queueing. Maintained temporal consistency in object IDs and action labels to support accurate behavior pattern analysis. This practice project developed my skills in video object tracking and action labeling, critical for training models in retail foot traffic and customer experience optimization.

Performed frame-by-frame video annotation on sample retail store footage, drawing bounding boxes around customers and staff, tracking movement paths across frames, and classifying actions such as browsing, product interaction, and queueing. Maintained temporal consistency in object IDs and action labels to support accurate behavior pattern analysis. This practice project developed my skills in video object tracking and action labeling, critical for training models in retail foot traffic and customer experience optimization.

2024

Image annotation for computer vision models

CVATImageBounding BoxEntity Ner Classification

Annotated over 20,000 images for e-commerce and computer vision models, applying precise bounding boxes, multi-class object detection, and attribute tagging. Maintained 98%+ accuracy accross high volume tasks

Annotated over 20,000 images for e-commerce and computer vision models, applying precise bounding boxes, multi-class object detection, and attribute tagging. Maintained 98%+ accuracy accross high volume tasks

2020 - 2024

Data Processing and Validation for AI service

OtherDocumentClassificationEvaluation Rating

Processed and validated document-sourced data (including PDFs, forms, surveys, reports, and customer feedback documents) for AI model training and analytics projects. Located and extracted relevant content from unstructured sources, confirmed document clarity, readability, completeness, and integrity before annotation; performed text cleaning/standardization, entity recognition (NER), sentiment analysis, and intent classification on over 15,000 entries. Ensured high data quality with 98%+ accuracy through strict guideline adherence, self-validation, and consistency checks. This involved handling document-based inputs (e.g., survey forms, review documents, exported reports) to create precise, model-ready datasets, skills directly transferable to collecting and validating publicly available PDF forms from official sources.

Processed and validated document-sourced data (including PDFs, forms, surveys, reports, and customer feedback documents) for AI model training and analytics projects. Located and extracted relevant content from unstructured sources, confirmed document clarity, readability, completeness, and integrity before annotation; performed text cleaning/standardization, entity recognition (NER), sentiment analysis, and intent classification on over 15,000 entries. Ensured high data quality with 98%+ accuracy through strict guideline adherence, self-validation, and consistency checks. This involved handling document-based inputs (e.g., survey forms, review documents, exported reports) to create precise, model-ready datasets, skills directly transferable to collecting and validating publicly available PDF forms from official sources.

2022 - 2023

Text Annotation for NLP Model in Saas/Tech

Label StudioTextEntity Ner ClassificationQuestion Answering

Annotated and classified over 15,000 text entries for customer feedback, surveys, and reviews, focusing on sentiment analysis, intent classification, and named entry recognition (NER) to support NLP models for churn prediction and customer retention in SaaS and tech applications.

Annotated and classified over 15,000 text entries for customer feedback, surveys, and reviews, focusing on sentiment analysis, intent classification, and named entry recognition (NER) to support NLP models for churn prediction and customer retention in SaaS and tech applications.

2021 - 2021

Education

Work History