Isaac O.

Key Skills

Software

CVAT

Top Subject Matter

Clinical/Healthcare Medical Data

Computer Vision & AI Data Annotation

Natural Language Processing (NLP) & Generative AI

Top Data Types

Text

Document

Audio

Top Task Types

Entity (NER) Classification

Bounding Box

Polygon

Segmentation

Classification

Polyline

Question Answering

RLHF

Data Collection

Text Generation

Evaluation/Rating

Transcription

Object Detection

Fine-tuning

Freelancer Overview

Detail-oriented AI Data Annotation Specialist and Fully Licensed Medical Doctor with experience in data annotation, quality assurance review, clinical documentation, transcription, and structured data workflows. Skilled in guideline-based annotation, dataset validation, text classification, entity recognition, content review, and AI training data preparation. Experienced in transforming complex information into accurate, structured, and high-quality datasets for AI and machine learning applications. Adept at maintaining consistency, handling edge cases, and supporting scalable annotation operations across healthcare and generalist AI workflows.

Labeling Experience

AI & Data Annotation Specialist (Healthcare NLP)

TextEntity (NER) Classification

Annotated and categorized clinical data including symptoms, diagnoses, and treatment plans to support healthcare AI development. Simulated medical NLP tasks such as entity recognition and clinical text classification for research and dataset creation. Consistently applied annotation guidelines to ensure medical data integrity and minimized labeling errors. • Annotated and labeled complex clinical records. • Simulated NLP-based tasks for data readiness. • Maintained data consistency and accuracy. • Reviewed and validated datasets for medical reliability.

2023 - Present

Video Annotation & CVAT Labeling Project

VideoPoint/Key Point

Supported computer vision model development through large-scale video annotation and object labeling workflows using CVAT for AI training and machine learning applications. Specific Data Labeling Tasks Performed: Frame-by-frame video annotation Bounding box annotation using CVAT Object tracking and motion labeling Video quality review and annotation correction Edge-case identification and validation QA review for annotation consistency Project Size: Worked on high-volume video datasets across multiple annotation tasks Managed large annotation queues involving continuous video segmentation and review workflows Quality Measures Adhered To: Maintained annotation precision and consistency across frames Followed project-specific labeling guidelines Performed quality audits and verification checks Reduced annotation errors through structured QA review processes

2025 - 2025

Document & PDF Annotation Project

DocumentEvaluation/Rating

Contributed to document intelligence workflows focused on structured document annotation, PDF labeling, and enterprise data organization for AI model training and automation systems. Specific Data Labeling Tasks Performed: PDF and document annotation Structured content labeling and categorization Data extraction and metadata tagging Workflow verification and annotation review Quality control and dataset validation Project Size: Processed and reviewed multiple batches of structured and semi-structured documents Managed high-volume annotation workflows involving document datasets Quality Measures Adhered To: Followed standardized annotation guidelines Performed multi-level QA checks Verified annotation consistency and completeness Maintained workflow accuracy and dataset reliability

2024 - 2025

Multimodal AI Evaluation & Image Annotation Project

ImageBounding Box

Participated in multimodal AI evaluation and image annotation workflows supporting machine learning and computer vision model training. Specific Data Labeling Tasks Performed: Bounding box image annotation Multimodal dataset evaluation Image classification and labeling Annotation review and validation Content moderation and structured tagging Project Size: Annotated and reviewed large image datasets across diverse AI evaluation workflows Supported ongoing annotation pipelines for model training datasets Quality Measures Adhered To: Applied guideline-based annotation standards Conducted QA checks and dataset validation Maintained labeling consistency and workflow accuracy Identified and corrected annotation edge cases

2023 - 2024

Education

Work History