Rogers W.

Key Skills

Software

Appen

Data Annotation Tech

Labelbox

Scale AI

CVAT

Prodigy

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Video

Top Task Types

Action Recognition

Classification

Data Collection

Emotion Recognition

Evaluation/Rating

Freelancer Overview

As a dedicated data labeling expert with a strong background in AI training data, I bring hands-on experience in annotating, validating, and curating high-quality datasets for machine learning and natural language processing models. I have contributed to diverse projects involving image classification, text categorization, sentiment analysis, named entity recognition (NER), and audio transcription. My attention to detail, consistency, and deep understanding of annotation guidelines ensure the delivery of accurate and scalable data crucial for model training and evaluation. I am proficient in various labeling platforms and tools such as Labelbox, Prodigy, SuperAnnotate, and CVAT, and I am skilled at following strict ontologies and adapting to evolving instructions. What sets me apart is my ability to combine domain expertise with quality assurance techniques to improve dataset integrity while meeting tight deadlines. My commitment to precision and data ethics makes me a reliable contributor to any AI development lifecycle.

Labeling Experience

Sentiment Classification and Content Rating for Online Reviews

ProdigyTextText SummarizationEvaluation Rating

Tagged and classified over 12,000 e-commerce and app reviews for sentiment (positive/negative/neutral), intent, and category (product feature, customer service, delivery, etc.). Also rated text quality for clarity and relevance using a 5-point rubric. Helped optimize training datasets for sentiment analysis and recommendation systems. Feedback loop with model trainers improved label consistency and model performance.

Tagged and classified over 12,000 e-commerce and app reviews for sentiment (positive/negative/neutral), intent, and category (product feature, customer service, delivery, etc.). Also rated text quality for clarity and relevance using a 5-point rubric. Helped optimize training datasets for sentiment analysis and recommendation systems. Feedback loop with model trainers improved label consistency and model performance.

2024 - 2025

Speech Emotion Dataset for Conversational AI

AppenAudioQuestion AnsweringEmotion Recognition

Labeled over 8,000 short audio clips to identify emotional tones such as happiness, sadness, anger, fear, and neutrality in various speakers. Utilized waveform inspection and linguistic cues to assign emotion labels while adhering to multilingual speaker guidelines. Participated in evaluator training to improve emotion detection accuracy, achieving 92% agreement with gold-standard labels across diverse dialects and acoustic conditions.

Labeled over 8,000 short audio clips to identify emotional tones such as happiness, sadness, anger, fear, and neutrality in various speakers. Utilized waveform inspection and linguistic cues to assign emotion labels while adhering to multilingual speaker guidelines. Participated in evaluator training to improve emotion detection accuracy, achieving 92% agreement with gold-standard labels across diverse dialects and acoustic conditions.

2024 - 2024

Annotation – Action Recognition in Surveillance Footage

CVATVideoBounding BoxSegmentation

Annotated over 2,500 video clips from surveillance feeds to identify and classify human actions such as walking, running, sitting, fighting, and loitering. Used frame-level temporal segmentation and bounding boxes to mark sequences. Followed strict schema for behavior tagging to train action-recognition models for public safety systems. Maintained over 96% inter-annotator agreement through regular calibration and QA reviews.

Annotated over 2,500 video clips from surveillance feeds to identify and classify human actions such as walking, running, sitting, fighting, and loitering. Used frame-level temporal segmentation and bounding boxes to mark sequences. Followed strict schema for behavior tagging to train action-recognition models for public safety systems. Maintained over 96% inter-annotator agreement through regular calibration and QA reviews.

2023 - 2024

Education

Work History