Person Deonta

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Entity Ner Classification

Freelancer Overview

I am a computational mathematician and AI scientist with extensive experience designing and optimizing machine learning systems, particularly in domains like finance, healthcare, and computer vision. My work has focused on developing mathematically rigorous frameworks for improving data quality, model robustness, and interpretability, often bridging abstract theory with practical data annotation and model training workflows. I have hands-on expertise in Python, MATLAB, R, and TensorFlow, and have led projects involving large-scale data environments, predictive modeling, and neural network optimization. My background includes collaborating with interdisciplinary teams to ensure high-quality labeled datasets and reliable AI solutions, as well as mentoring others in advanced modeling and data analysis techniques. I am passionate about applying mathematical principles to enhance the reliability and effectiveness of AI systems, especially through careful attention to the quality and structure of training data.

Labeling Experience

Text Annotation & LLM Evaluation for AI Training

Scale AITextEntity Ner Classification

Worked on large-scale text annotation projects supporting the training and evaluation of Large Language Models (LLMs). Responsibilities included performing Named Entity Recognition (NER) by accurately identifying and labeling entities such as people, organizations, locations, dates, and domain-specific terms within text datasets. Evaluated and validated annotated outputs to ensure consistency, accuracy, and compliance with detailed project guidelines. Contributed to improving model understanding of language structure, context, and semantic relationships. Maintained high annotation quality while meeting productivity and accuracy benchmarks in a fast-paced, remote AI training environment.

Worked on large-scale text annotation projects supporting the training and evaluation of Large Language Models (LLMs). Responsibilities included performing Named Entity Recognition (NER) by accurately identifying and labeling entities such as people, organizations, locations, dates, and domain-specific terms within text datasets. Evaluated and validated annotated outputs to ensure consistency, accuracy, and compliance with detailed project guidelines. Contributed to improving model understanding of language structure, context, and semantic relationships. Maintained high annotation quality while meeting productivity and accuracy benchmarks in a fast-paced, remote AI training environment.

2023 - 2024

Education

Work History