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I am a Detail Orientation Data Annotation Specialist with first-person experience on labeling and reviewing large image and video datasets for AI and machine learning model training. I focus on accuracy, consistency, and strict adherence to complex annotation guidelines using tools and workflows that maintain dataset quality. I have successfully labeled 2,000+ data episodes with an 85% accuracy rate, and I am proficient in data analysis, labeling tools, and Microsoft Excel. I also work closely with quality assurance teams to resolve feedback and continuously improve data reliability and performance for AI development.…I am a Detail Orientation Data Annotation Specialist with first-person experience on labeling and reviewing large image and video datasets for AI and machine learning model training. I focus on accuracy, consistency, and strict adherence to complex annotation guidelines using tools and workflows that maintain dataset quality. I have successfully labeled 2,000+ data episodes with an 85% accuracy rate, and I am proficient in data analysis, labeling tools, and Microsoft Excel. I also work closely with quality assurance teams to resolve feedback and continuously improve data reliability and performance for AI development.

Okunaiya Samuel

Data Collector, Data Annotator, Data Engineer, +3

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I am a Detail Orientation Data Annotation Specialist with first-person experience on labeling and reviewing large image and video datasets for AI and machine learning model training. I focus on accuracy, consistency, and strict adherence to complex annotation guidelines using tools and workflows that maintain dataset quality. I have successfully labeled 2,000+ data episodes with an 85% accuracy rate, and I am proficient in data analysis, labeling tools, and Microsoft Excel. I also work closely with quality assurance teams to resolve feedback and continuously improve data reliability and performance for AI development.…I am a Detail Orientation Data Annotation Specialist with first-person experience on labeling and reviewing large image and video datasets for AI and machine learning model training. I focus on accuracy, consistency, and strict adherence to complex annotation guidelines using tools and workflows that maintain dataset quality. I have successfully labeled 2,000+ data episodes with an 85% accuracy rate, and I am proficient in data analysis, labeling tools, and Microsoft Excel. I also work closely with quality assurance teams to resolve feedback and continuously improve data reliability and performance for AI development.

Available to hire

I am a Detail Orientation Data Annotation Specialist with first-person experience on labeling and reviewing large image and video datasets for AI and machine learning model training. I focus on accuracy, consistency, and strict adherence to complex annotation guidelines using tools and workflows that maintain dataset quality.

I have successfully labeled 2,000+ data episodes with an 85% accuracy rate, and I am proficient in data analysis, labeling tools, and Microsoft Excel. I also work closely with quality assurance teams to resolve feedback and continuously improve data reliability and performance for AI development.

Skills

AI Data Labelling

Microsoft Excel

AI Collection (Video)

Data Visualization

AI Collection (Audio)

Experience Level

AI Data Labelling

Expert

Microsoft Excel

Expert

AI Collection (Video)

Expert

Data Visualization

Beginner

AI Collection (Audio)

Beginner

Language

Work Experience

Data Annotation Specialist / Data Annotator (AI Data Collection) at AI Data Collector

December 1, 2025 - Present

Engaged in ongoing AI data collection and annotation work, capturing diverse real-world image and video activities according to detailed project guidelines for AI model training. Organized, uploaded, and documented datasets using project-specific data collection platforms. Worked closely with quality assurance teams to address feedback and continuously improve dataset quality and overall model accuracy.

AI Data Collector (Data Annotator / Data Collection)

December 1, 2025 - Present

Geographically focused, first-person, observation-based data collection for AI model training; captured diverse real-world activities and environments. Followed detailed project guidelines for image and video data capture. Organized, uploaded, and documented collected datasets using project-specific data collection platforms, and incorporated feedback from quality assurance teams to improve data quality and model accuracy.

Data Annotator / AI Data Collection / Data Collector (based on resume headings)

December 1, 2025 - Present

Collected first-person, egocentric image and video data following detailed project guidelines for AI model training. Captured diverse real-world activities and environments to improve dataset quality and model accuracy. Organized, uploaded, and documented datasets using project-specific data collection platforms. Worked closely with quality assurance teams to address feedback and continuously improve data quality.

Data Annotator at AI

February 1, 2025 - Present

Labeled and reviewed 2,000+ data episodes used for machine learning and AI model training. Maintained an 85% accuracy rate across multiple annotation projects. Performed image and video annotation tasks following strict dataset guidelines, and conducted dataset reviews to ensure labeling consistency and high data quality. Used structured labeling tools to categorize and tag training data efficiently, and improved dataset reliability by identifying labeling errors and correcting inconsistencies. Demonstrated strong attention to detail while following complex annotation instructions.

Data Annotator

February 1, 2025 - Present

Labeled and reviewed 2,000+ data episodes for machine learning and artificial intelligence model training, maintaining about an 85% accuracy rate across multiple annotation projects. Performed image and video data annotation tasks following strict dataset guidelines. Conducted dataset reviews to ensure labeling consistency and high data quality. Used structured labeling tools to categorize and tag training data efficiently, and improved reliability by identifying labeling errors and correcting inconsistencies. Demonstrated strong attention to detail when working with complex annotation instructions.