George Allen David

Key Skills

Software

Labelbox

Other

Top Subject Matter

Insurance Claims

Medical Documents

Police Reports

Top Data Types

Document

Text

Top Task Types

Entity (NER) Classification

Freelancer Overview

Claims Document Annotation & Data Extraction Project Lead. Brings 12+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, Internal, and Proprietary Tooling. Education includes Master of Science, Northwest Missouri State University (2022) and Bachelor of Science, Northwest Missouri State University (1998). AI-training focus includes data types such as Document and Text and labeling workflows including Entity (NER) Classification, Evaluation, and Rating.

Labeling Experience

Claims Document Annotation & Data Extraction Project Lead

LabelboxDocumentEntity Ner Classification

Led a data annotation project to extract structured data from unstructured medical reports, police accident reports, and claims forms for an insurtech startup. Developed detailed guidelines for annotating and classifying critical fields, ensuring consistency and quality in the labeled data. Achieved extremely high accuracy, enabling the client to build a robust NLP model for automated claims processing. • Extracted data points like dates of service, ICD-10 codes, procedure codes, and claimant statements • Managed annotation of over 5,000 documents • Produced training data with 99.5% accuracy for NLP model • Oversaw annotation guideline design and workflow improvements

Led a data annotation project to extract structured data from unstructured medical reports, police accident reports, and claims forms for an insurtech startup. Developed detailed guidelines for annotating and classifying critical fields, ensuring consistency and quality in the labeled data. Achieved extremely high accuracy, enabling the client to build a robust NLP model for automated claims processing. • Extracted data points like dates of service, ICD-10 codes, procedure codes, and claimant statements • Managed annotation of over 5,000 documents • Produced training data with 99.5% accuracy for NLP model • Oversaw annotation guideline design and workflow improvements

2023 - Present

LLM Audit for P&C Pricing Summaries Lead Validator

Text

Served as Lead Actuarial Validator for auditing LLM-generated insurance pricing summaries from unstructured policy data. Developed an actuarially-based scoring rubric and manually evaluated over 300 model outputs for logical consistency and completeness. Provided critical validation feedback that directly informed model retraining and quality improvement. • Reviewed summaries for accuracy, risk factor omission, and clarity • Worked cross-functionally with LLM developers and data scientists • Composed a comprehensive validation report with findings and action items • Helped drive a 40% improvement in model output quality

Served as Lead Actuarial Validator for auditing LLM-generated insurance pricing summaries from unstructured policy data. Developed an actuarially-based scoring rubric and manually evaluated over 300 model outputs for logical consistency and completeness. Provided critical validation feedback that directly informed model retraining and quality improvement. • Reviewed summaries for accuracy, risk factor omission, and clarity • Worked cross-functionally with LLM developers and data scientists • Composed a comprehensive validation report with findings and action items • Helped drive a 40% improvement in model output quality

2023 - 2023

Annotation Rules Designer and Subject Matter Expert for AI Underwriting Tool

OtherDocumentEntity Ner Classification

Defined detailed annotation rules for extracting key risk factors from unstructured insurance submission documents. Acted as the subject matter expert to ensure correct annotation of data for an AI underwriting tool. Supervised the labeling process for high-quality training data crucial to model success. • Focused on documents like loss runs, financial statements, and inspection reports • Ensured annotation captured risk-relevant information accurately • Guided team on annotation standards and practices • Contributed to foundational model training for automated underwriting

Defined detailed annotation rules for extracting key risk factors from unstructured insurance submission documents. Acted as the subject matter expert to ensure correct annotation of data for an AI underwriting tool. Supervised the labeling process for high-quality training data crucial to model success. • Focused on documents like loss runs, financial statements, and inspection reports • Ensured annotation captured risk-relevant information accurately • Guided team on annotation standards and practices • Contributed to foundational model training for automated underwriting

2019 - 2021

Education

Work History