AI Data Quality

 Responsibilities

Strategic Leadership:

• Drive development, refinement, and documentation of quality assurance processes and standard operating procedures to ensure high-quality outputs.
• Establish comprehensive quality metrics (e.g., F1 score, inter-annotator agreement) aligned with business objectives and industry standards.
• Continuously review and improve annotation workflows to identify risks, enhance efficiency, and reduce errors.
• Serve as the subject matter expert on annotation quality; provide ongoing feedback, training, and support to annotators and project teams.

Analysis & Reporting:

• Lead in-depth data analysis to diagnose quality issues, evaluate quality strategies, and identify root causes of recurring errors.
• Develop and maintain dashboards providing real-time insights on quality metrics and project performance.
• Prepare and present strategic quality reports to senior management and clients, highlighting trends, risks, and improvement plans.
• Collaborate with operational management, engineering, and client services to align on project goals and quality initiatives.

Operational Leadership:

• Lead and mentor a team of Data Quality Analysts, fostering a culture of continuous improvement and accountability.
• Manage configuration and integration of annotation and quality control tools (e.g., Labelbox, Dataloop, LabelStudio), ensuring optimal performance.
• Identify and implement innovative quality control tools and automation technologies to streamline workflows and improve efficiency.