Earl Duhamel
DataAnnotation.tech
Beaufort,SC
1
Language
1
Certification
Dedicated Data Annotator with a strong focus on data labeling, quality assurance, and problem resolution. Experienced in utilizing annotation tools and maintaining compliance with data privacy policies to ensure high standards and accuracy in large datasets.
Organized and dependable candidate successful at managing multiple priorities with a positive attitude. Willingness to take on added responsibilities to meet team goals.
Work History
Data Annotator
1 Month- Annotated datasets to enhance machine learning model training accuracy.
- Reviewed and corrected annotation inconsistencies to ensure data quality.
- Participated in training sessions to improve understanding of project requirements.
- Utilized annotation tools to efficiently categorize large volumes of data.
- Conducted quality checks on annotated data to maintain high standards.
- Documented issues encountered during annotation for future reference and resolution.
- Demonstrated strong attention to detail when working on complex projects involving multiple layers of information.
- Ensured adherence to data privacy policies while handling sensitive information during the annotation process.
- Enhanced data accuracy by meticulously annotating and labeling various data types.
- Checked for accuracy by verifying data and records.
- Compared transcribed data with source document to detect and correct errors.
- Followed data entry protocols, rules and regulations.
Education
- Data Analysis
Skills
Data labeling
Data quality
Data entry
Data analysis
Pattern recognition
Data privacy compliance
Ethical practices
Detail orientation
Problem resolution
Cross-functional collaboration
Analytical thinking
Document proofreading
Certification
Forage Academy Data Labeling Job Simulation on Forage August 19, 2026
- Completed a job simulation where I worked as a Data Labeling Analyst for a hypothetical AI company, reviewing real customer support messages
- Practiced classifying messages for Intent, Sentiment, and PII (personally identifiable information) using a consistent labeling schema
- Evaluated tricky or ambiguous edge cases, and wrote short rationales justifying final label decisions
- Reviewed labeling work done by others, identified inconsistencies, and suggested one improvement to the team’s labeling guidelines
- Strengthened my ability to apply clear definitions, follow ethical privacy practices, and maintain consistency across data
- Gained hands-on experience in human-AI collaboration, foundational to roles in AI ops, trust & safety, and machine learning support
Languages
English
Native or Bilingual
Timeline
Data Annotator
DataAnnotation.tech
07.2026 - CurrentRead More
Homeschool
from Data Analysis
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