Julie Dishaw

Key Skills

Software

Scale AI

LightTag

Top Subject Matter

No subject matter listed

Top Data Types

Text

Document

Top Label Types

Entity Ner Classification

Relationship

Text Generation

Text Summarization

Bounding Box

Freelancer Overview

I am an experienced remote professional with a strong background in managing and evaluating large volumes of data through high-frequency client communications and technical support roles. My expertise includes data collection, preprocessing, and quality assurance, developed through handling complex customer inquiries and troubleshooting technical issues across multiple channels. I excel at maintaining accuracy and attention to detail under pressure, and I am adept at multitasking, critical thinking, and prioritizing tasks independently. My experience has made me highly adaptable and service-driven, with a proven ability to work efficiently in fast-paced, remote environments—skills that translate directly to data labeling and AI training data projects. I am committed to delivering precise, reliable results while ensuring data integrity and client satisfaction.

Labeling Experience

docs

LighttagDocumentBounding BoxEntity Ner Classification

Focused on the curation, annotation, and labeling of raw, unstructured, and semi-structured documents (e.g., invoices, legal contracts, reports, forms) to create high-quality training data for Natural Language Processing (NLP) and Optical Character Recognition (OCR) machine learning models.

Focused on the curation, annotation, and labeling of raw, unstructured, and semi-structured documents (e.g., invoices, legal contracts, reports, forms) to create high-quality training data for Natural Language Processing (NLP) and Optical Character Recognition (OCR) machine learning models.

2024 - 2025

text

Scale AITextEntity Ner ClassificationRelationship

Build an end-to-end NLP pipeline that automatically analyzes incoming customer support emails or chatbot messages. The system will categorize the urgency of the ticket, classify it into specific functional departments (e.g., Billing, Technical Support, General Query), and determine the sentiment (Positive, Negative, Neutral) of the customer.

Build an end-to-end NLP pipeline that automatically analyzes incoming customer support emails or chatbot messages. The system will categorize the urgency of the ticket, classify it into specific functional departments (e.g., Billing, Technical Support, General Query), and determine the sentiment (Positive, Negative, Neutral) of the customer.

2023 - 2024

Education

Work History