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Shana Mcneill

Data Annotation Specialist

USA flagMc Intosh, Usa
Expert

Key Skills

Software

No software listed

Top Subject Matter

AI model image training
Natural Language Processing (NLP)
AI data tagging and quality control

Top Data Types

ImageImage
TextText

Top Task Types

ClassificationClassification
Entity (NER) ClassificationEntity (NER) Classification

Freelancer Overview

Data Annotation Specialist. Core strengths include Internal and Proprietary Tooling. Education includes Master of Science, University of California and Bachelor of Science, University of Washington. AI-training focus includes data types such as Image and Text and labeling workflows including Classification and Entity (NER) Classification.

Expert

Labeling Experience

Data Annotation Specialist

ImageClassification
As a Data Annotation Specialist, I was responsible for annotating thousands of images monthly for AI model training. My attention to detail and application of advanced QC strategies resulted in a significant boost to data accuracy. I contributed to team efficiency by sharing annotation best practices and training new members. • Labeled over 10,000+ images every month with a focus on precision. • Improved data accuracy by 15% through structured quality control. • Trained 3 team members on annotation tools and workflows. • Utilized internal annotation software tailored for image data.

As a Data Annotation Specialist, I was responsible for annotating thousands of images monthly for AI model training. My attention to detail and application of advanced QC strategies resulted in a significant boost to data accuracy. I contributed to team efficiency by sharing annotation best practices and training new members. • Labeled over 10,000+ images every month with a focus on precision. • Improved data accuracy by 15% through structured quality control. • Trained 3 team members on annotation tools and workflows. • Utilized internal annotation software tailored for image data.

2024 - 2025

Machine Learning Labeler

TextEntity Ner Classification
As a Machine Learning Labeler, I was tasked with tagging thousands of text entries for natural language processing projects. I played a key role in reducing error rates and driving improvements in the labeling workflow. Collaboration across teams helped optimize our software tools and annotation efficiency. • Tagged 8,000 text samples per month for NLP model development. • Cut dataset error rates by 12% via consistent annotation checks. • Worked closely with colleagues to refine labeling processes. • Used internal proprietary text labeling tools.

As a Machine Learning Labeler, I was tasked with tagging thousands of text entries for natural language processing projects. I played a key role in reducing error rates and driving improvements in the labeling workflow. Collaboration across teams helped optimize our software tools and annotation efficiency. • Tagged 8,000 text samples per month for NLP model development. • Cut dataset error rates by 12% via consistent annotation checks. • Worked closely with colleagues to refine labeling processes. • Used internal proprietary text labeling tools.

2022 - 2024

Data Tagging Analyst

TextClassification
In my role as Data Tagging Analyst, I managed a range of labeling projects to maximize data return on investment for AI initiatives. Methodical quality assurance steps were used to achieve exceptionally high data precision. Conducting regular audits ensured consistency and reliability in tagged datasets. • Oversaw and coordinated 6 distinct data labeling projects. • Achieved a 95% precision rate across all reviewed datasets. • Performed systematic project audits for quality maintenance. • Deployed internal labeling tools for text annotation.

In my role as Data Tagging Analyst, I managed a range of labeling projects to maximize data return on investment for AI initiatives. Methodical quality assurance steps were used to achieve exceptionally high data precision. Conducting regular audits ensured consistency and reliability in tagged datasets. • Oversaw and coordinated 6 distinct data labeling projects. • Achieved a 95% precision rate across all reviewed datasets. • Performed systematic project audits for quality maintenance. • Deployed internal labeling tools for text annotation.

2021 - 2022

Education

U

University of Washington

Bachelor of Science, Computer Science

Bachelor of Science
Not specified
U

University of California

Master of Science, Data Science

Master of Science
Not specified

Work History

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