AI Data Quality Analyst
Tasks
- Analyze annotation errors and trends
- Collaborate with stakeholders on project requirements
- Conduct root cause analysis for annotation errors
- Evaluate data quality using quality metrics
- Identify opportunities for automation in analytics
- Improve annotation guidelines and quality standards
- Maintain documentation for quality standards
- Perform quality audits on annotated datasets
- Provide feedback across the annotation pipeline
- Report data quality metrics and insights
- Resolve edge-case annotations
- Train annotators
- Use annotation and quality control tools
Perks/Benefits
Skills/Tech-stack
AI/ML | AI/ML Pipelines | Agile | Automation | Cause analysis | Data Annotation | Data Documentation | Data Quality | Dataloop | F1-score | Guideline Development | Inter-annotator agreement | LLMs | Label Studio | Labelbox | ML pipelines | Multimodal Data | Prompt engineering | Python | Quality Control | Quality metrics | Root Cause Analysis | Root cause | Statistical Analysis
Education
Bachelor of Arts | Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Data Quality Analyst | Analyst | Data Quality Analyst | Quality Analyst
Regions
Countries
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