Data Quality Analyst at Amazon
AI training data quality review, annotation accuracy validation, error-pattern analysis, and quality benchmarking.
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Data Quality Analyst at Amazon. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, Appen, and Don't disclose. Education includes Master of Science, Florida Institute of Technology (2022) and Bachelor of Science, University of Central Florida (2019). AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and Classification.
AI training data quality review, annotation accuracy validation, error-pattern analysis, and quality benchmarking.
Text and image categorization, search-relevance labeling, AI response evaluation, and annotation taxonomy application.
Sentiment and intent categorization, dataset cleaning, deduplication, and formatting standardization.
Conversational AI response quality evaluation, hallucination and bias identification, factual consistency review, and prompt evaluation.
Master of Science, Artificial Intelligence
Bachelor of Science, Information Technology
Data Quality Analyst
Senior Assistant