🕒 yesterday
Build and maintain BigQuery data models, marketing and sales pipelines, and Looker Studio reporting while investigating discrepancies, improving data quality, and explaining metrics to business teams.
AI Data Quality Specialist - English (Philippines)
📅 Mar 10
English Language ReviewData AnnotationSentiment AnalysisFactual Accuracy Assessment
📜 Description
- Review and label content for sentiment, factual accuracy, and reasoning issues.
- Evaluate model outputs across quality dimensions using scoring frameworks.
- Validate automated assessments and identify discrepancies or errors.
🛠️ Requirements
- Advanced English proficiency (reading and writing) with professional-level fluency.
- Strong comprehension and articulation skills, with the ability to provide clear and well-reasoned written justifications.
- Previous experience in data annotation, AI training, or related projects.
- Preferred experience in analytical fields such as linguistics, journalism, research, or similar domains.
- Exceptional attention to detail and accuracy.
- Proven analytical reasoning and critical thinking abilities.
- Consistency in decision-making across high volumes of tasks.
- Familiarity with AI systems, large language models (LLMs), or evaluation processes (preferred but not required).
Full job description
Contract Type: Freelance
Language: English (Philippines)
About the Role
We are seeking a detail-oriented AI Data Quality & Annotation Specialist to help develop and evaluate large language models (LLMs). Your role will involve reviewing English language content to assess sentiment accuracy, factual consistency, and reasoning quality using structured guidelines. This position is ideal for individuals with sharp analytical skills, attention to detail, and the ability to consistently apply labeling standards.
What You Will Do:
- Review and label content for sentiment, factual accuracy, and reasoning issues.
- Evaluate model outputs across quality dimensions using scoring frameworks.
- Validate automated assessments and identify discrepancies or errors.
Requirements:
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