AI Data Quality Specialist

1 week ago

Barcelona, Philippines beBeeDataQuality Full time €60,000 - €90,000

We are seeking a detail-oriented AI Data Quality Specialist to join our team and drive continuous improvements in AI data.

Job Responsibilities:
  • The successful candidate will perform quality audits on annotated datasets, ensuring they meet established guidelines and quality benchmarks.
  • Metrics such as F1 score and inter-annotator agreement will be used to evaluate data quality.
  • Annotation errors, trends, and project documentation will be analyzed to identify root causes and propose remediation strategies.
  • The specialist will resolve and analyze edge-case annotations to ensure quality and identify improvement areas.
  • Proficient use of annotation and quality control tools will be required to perform reviews and track quality metrics.
  • Feedback will be provided for clarifications or improvements of project guidelines.
  • Automation opportunities will be identified and contributed to the documentation of quality standards and procedures.

Required Skills and Qualifications:

  • 1+ years of experience as a data analyst with exposure to data quality and/or data annotation, ideally in an AI/ML context.
  • Familiarity with basic concepts of AI/ML pipelines and data.
  • Strong analytical and problem-solving skills with an exceptional eye for detail.
  • Excellent written and verbal communication skills to clearly articulate quality issues and collaborate with diverse teams.
  • Ability to work independently and manage time effectively to meet deadlines.
  • Strong problem-solving, critical thinking, and continuous optimization mindset.
  • Ability to work independently in a fast-paced environment with a focus on detail and strategic priorities.
  • Positive can-do attitude and adaptability to new environments.

Nice to Have:

  • Familiarity with data annotation tools (e.g., Labelbox, Dataloop, LabelStudio).
  • Experience with multi-modal AI/ML datasets (images, videos, text, audio).
  • Experience in agile or fast-paced tech environments with exposure to AI/ML pipelines.
  • Knowledge of programming languages (e.g., Python).
  • Understanding of data quality concepts for AI/ML models and their impact on model performance.
  • Knowledge of quality metrics and statistical methods used in AI/ML data quality.
  • Experience with data for AI/ML models and prompt engineering or leveraging LLMs.

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