Jarrod Munoz

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Polygon

Segmentation

Classification

Transcription

Freelancer Overview

I am a detail-oriented data entry and data management specialist with over 5 years of experience supporting high-volume, accuracy-critical projects across business services, healthcare, and real estate. My background includes processing and verifying large datasets, implementing rigorous quality control protocols, and leading database cleanup and digital conversion initiatives to ensure data integrity—skills directly applicable to data labeling and AI training data environments. I am proficient with tools such as Microsoft Excel (pivot tables, VLOOKUP), Salesforce, QuickBooks, SharePoint, Google Sheets, and various document management systems. I have consistently maintained a 99.5% accuracy rate while handling confidential information, training staff on best practices, and optimizing data workflows. My experience in developing standardized processes and documentation for data quality, along with a strong commitment to efficiency and privacy, enables me to deliver reliable, high-quality annotated data for AI and machine learning projects.

Labeling Experience

Senior Text Annotation Specialist - LLM Training & Quality Assurance

Data Annotation TechTextPolygonSegmentation

A large-scale text annotation project supporting enterprise LLM development, involving classification, segmentation, and transcription tasks across 750,000+ text samples. Core responsibilities included multi-label categorization into 35 intent classes, semantic segmentation of legal and customer service documents, and transcription of handwritten feedback. Project quality was maintained through daily randomized QA checks on 10% of annotations, gold-standard dataset calibration targeting 98% accuracy, and weekly team calibration sessions. Delivered all milestones ahead of schedule with 96% inter-annotator agreement while adhering to GDPR privacy protocols.

A large-scale text annotation project supporting enterprise LLM development, involving classification, segmentation, and transcription tasks across 750,000+ text samples. Core responsibilities included multi-label categorization into 35 intent classes, semantic segmentation of legal and customer service documents, and transcription of handwritten feedback. Project quality was maintained through daily randomized QA checks on 10% of annotations, gold-standard dataset calibration targeting 98% accuracy, and weekly team calibration sessions. Delivered all milestones ahead of schedule with 96% inter-annotator agreement while adhering to GDPR privacy protocols.

2023 - 2024

Education

Work History