Morgan Anderson

Key Skills

Software

Label Studio

Prodigy

Top Subject Matter

Natural Language Processing

Computer Vision

Nlp Domain Expertise

Top Data Types

Text

Image

Document

Top Task Types

Entity Ner Classification

Classification

Freelancer Overview

Machine Learning Operations Specialist, TechVision AI Solutions. Brings 5+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Label Studio and Prodigy. Education includes Master of Science, Fairleigh Dickinson University (2025) and Bachelor of Science, Augusta University (2022). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Entity (NER) Classification.

Labeling Experience

Machine Learning Operations Specialist, TechVision AI Solutions

Label StudioText

I designed and implemented data annotation workflows for large-scale NLP and computer vision projects for Fortune 500 clients. I developed annotation guidelines and quality control frameworks to ensure consistency and accuracy. I systematically evaluated LLM outputs and implemented programmatic labeling strategies to optimize the annotation process. • Achieved 94% inter-annotator agreement across distributed teams. • Managed datasets exceeding 500,000 labeled examples. • Reduced manual annotation requirements by 60% through programmatic labeling. • Collaborated with ML engineers to design targeted data collection strategies.

I designed and implemented data annotation workflows for large-scale NLP and computer vision projects for Fortune 500 clients. I developed annotation guidelines and quality control frameworks to ensure consistency and accuracy. I systematically evaluated LLM outputs and implemented programmatic labeling strategies to optimize the annotation process. • Achieved 94% inter-annotator agreement across distributed teams. • Managed datasets exceeding 500,000 labeled examples. • Reduced manual annotation requirements by 60% through programmatic labeling. • Collaborated with ML engineers to design targeted data collection strategies.

2023 - Present

Data Annotation Specialist (Contract), DataForce by TransPerfect

Label StudioTextClassification

I performed high-quality data annotation across text, image, and audio modalities for ML training datasets in contract projects. I specialized in complex NLP tasks like semantic similarity, intent classification, entity linking, and relevance judgment. I provided actionable feedback on ambiguous cases to refine annotation frameworks. • Consistently exceeded 97% accuracy rates and met production deadlines. • Covered multi-modal data annotation in multiple projects. • Worked remotely to annotate datasets according to detailed guidelines. • Improved annotation frameworks with direct quality feedback.

I performed high-quality data annotation across text, image, and audio modalities for ML training datasets in contract projects. I specialized in complex NLP tasks like semantic similarity, intent classification, entity linking, and relevance judgment. I provided actionable feedback on ambiguous cases to refine annotation frameworks. • Consistently exceeded 97% accuracy rates and met production deadlines. • Covered multi-modal data annotation in multiple projects. • Worked remotely to annotate datasets according to detailed guidelines. • Improved annotation frameworks with direct quality feedback.

2022 - 2023

NLP Research Assistant, Augusta University

ProdigyTextEntity Ner Classification

I led data collection and annotation initiatives for research projects in sentiment analysis, named entity recognition, and text classification. I labeled and validated over 50,000 text samples following detailed protocols and developed Python tools for data preprocessing and quality assurance. I evaluated fine-tuned NLP models and co-authored research on weak supervision techniques. • Labeled data included social media, news articles, and scientific documents. • Calculated inter-annotator agreement to ensure labeling accuracy. • Presented research at ACL and EMNLP workshops. • Provided feedback for model improvement and edge case handling.

I led data collection and annotation initiatives for research projects in sentiment analysis, named entity recognition, and text classification. I labeled and validated over 50,000 text samples following detailed protocols and developed Python tools for data preprocessing and quality assurance. I evaluated fine-tuned NLP models and co-authored research on weak supervision techniques. • Labeled data included social media, news articles, and scientific documents. • Calculated inter-annotator agreement to ensure labeling accuracy. • Presented research at ACL and EMNLP workshops. • Provided feedback for model improvement and edge case handling.

2021 - 2023

Education

Work History