Data Quality Specialist for AI Projects

7 days ago

Dasmariñas, Calabarzon, Philippines beBeeQuality Full time ₱60,000 - ₱99,999

Establishing high-quality annotated datasets is crucial for the success of AI projects. As a Data Quality Specialist, you will be responsible for ensuring the accuracy and consistency of these datasets through meticulous quality audits, statistical analysis, and root cause investigation.

About the Role

The ideal candidate will possess strong analytical skills, excellent communication abilities, and the capacity to work independently with minimal supervision. They will collaborate closely with project stakeholders to understand project requirements and identify areas for improvement in the annotation pipeline.

This role is suitable for individuals with 1+ years of experience in data analysis, preferably in an AI/ML context. The successful candidate should have a keen eye for detail, be able to manage time effectively, and demonstrate a passion for continuous learning and improvement.

Key Responsibilities
  • Quality Audits: Conduct thorough quality audits on annotated datasets to ensure they meet established guidelines and quality benchmarks.
  • Statistical Analysis: Leverage statistical based quality metrics such as F1 score and inter-annotator agreement to evaluate data quality.
  • Root Cause Investigation: Analyze annotation errors, trends, project processes, and project documentation to identify and understand the root cause of errors and propose remediation strategies.
  • Edge-Case Management: Resolve and analyze edge-case annotations to ensure quality and identify areas for improvement.
  • Tooling Expertise: Develop proficiency in using annotation and quality control tools to perform reviews and track quality metrics.
Continuous Improvement
  • Automation Opportunities: Identify opportunities to use automation to enhance analytics, provide deeper insights, and improve efficiency.
  • Documentation Development: Create and maintain up-to-date documentation on quality standards, annotation guidelines, and quality control procedures.
  • Feedback Mechanism: Provide regular feedback that identifies areas for improvement across the annotation pipeline.
Collaboration & Communication
  • Cross-Functional Teamwork: Work collaboratively with key project stakeholders and clients to understand project requirements and improve annotation pipelines.
  • Training Support: Assist with training annotators, providing guidance, feedback, and support to ensure data quality.
  • Reporting: Deliver regular updates that highlight data quality metrics, key findings, and actionable insights for continuous process improvements.

TaskUs offers a competitive industry salary, comprehensive benefits package, and a supportive environment that fosters internal mobility and professional growth. If you are passionate about data quality and AI/ML, we encourage you to apply today

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