AI Data Quality Lead Position

20 hours ago

Manila, National Capital Region, Philippines beBeeDataQuality Full time $80,000 - $100,000
Job Title:

Data Quality Engineer & Leader

Job Description:

The role of the Data Quality Engineer and Leader is to create high-quality data workflows, ensuring reliable AI models are built and evaluated without compromising speed or efficiency.

Key Responsibilities:

  • Develop quality assurance processes and standards to ensure high-quality outputs.
  • Establish quality metrics that align with business objectives and industry standards.
  • Review annotation workflows to identify risks and opportunities for improvement.
  • Act as a subject matter expert on annotation quality, providing feedback and support to annotators and project teams.

Analysis & Reporting:

  • Lead in-depth data analysis to diagnose quality issues and assess strategy effectiveness.
  • Develop dashboards for real-time quality metrics and project performance.
  • Prepare strategic quality reports to senior management and clients, outlining quality trends and improvement plans.
  • Collaborate with cross-functional teams to align on project goals and QA initiatives.

Operational Leadership:

  • Lead a team of Data Quality Analysts, providing mentorship and training.
  • Manage configuration and integration of annotation tools to ensure optimal performance.
  • Identify and implement innovative quality control tools and automation technologies.

Requirements:

  • 3+ years in data quality management or related AI/ML roles.
  • Proven experience designing QA strategies for large-scale data annotation projects.
  • Leadership experience managing remote teams.
  • Deep understanding of data annotation processes, QA methodologies, and metrics.
  • Strong data-analysis skills and ability to translate findings into recommendations.
  • Excellent communication skills with experience presenting data insights to stakeholders.
  • Proficiency with annotation and QA tools.
  • High proficiency in Excel and Google Sheets; familiarity with Python and SQL.
  • Familiarity with AI/ML pipeline concepts including data prep, model training, and evaluation.

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