Valton Owens

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Polygon

Polyline

Cuboid

Freelancer Overview

I am an experienced data scientist and applied mathematician with a deep background in building and leading teams that develop high-quality AI and machine learning solutions. My expertise spans over 20 years and includes hands-on work with data labeling, annotation, and prompt engineering for NLP and LLM-powered applications, such as financial document analyzers and math problem-solving tools. I have designed and checked generative AI prompts, implemented entity extraction and relation mapping pipelines, and built automated data quality and monitoring frameworks to ensure the reliability of training data in production environments. My technical toolkit includes Python, SQL, MATLAB, TensorFlow, PyTorch, Hugging Face Transformers, and cloud platforms like AWS and GCP. I combine strong mathematical foundations with practical experience in NLP, financial analytics, and real-time anomaly detection, enabling me to deliver robust, accurate, and scalable AI training data solutions.

Labeling Experience

healthcare annotation

Data Annotation TechImageBounding BoxPolygon

Participated in healthcare-focused image annotation projects, applying board-certified expertise in cardiology and general surgery to accurately label and segment medical imaging datasets. Annotated cardiac ultrasound (echocardiography), CT angiography, and MRI scans, identifying and bounding key anatomical structures, valvular abnormalities, coronary artery disease markers, and subtle pathologies with high precision. Contributed to surgical image datasets by labeling intraoperative videos and fluoroscopy images, marking instruments, anatomical landmarks, and procedural steps in general and minimally invasive surgeries. Maintained consistent accuracy (>98%) across high-volume tasks while adhering to strict project guidelines, ensuring high-quality training data for diagnostic AI models.

Participated in healthcare-focused image annotation projects, applying board-certified expertise in cardiology and general surgery to accurately label and segment medical imaging datasets. Annotated cardiac ultrasound (echocardiography), CT angiography, and MRI scans, identifying and bounding key anatomical structures, valvular abnormalities, coronary artery disease markers, and subtle pathologies with high precision. Contributed to surgical image datasets by labeling intraoperative videos and fluoroscopy images, marking instruments, anatomical landmarks, and procedural steps in general and minimally invasive surgeries. Maintained consistent accuracy (>98%) across high-volume tasks while adhering to strict project guidelines, ensuring high-quality training data for diagnostic AI models.

2023 - 2025

Education

Work History