AI Data Quality Professional

2 weeks ago

Porac, Philippines beBeeData Full time $61,000 - $77,000
Job Description:

Maintaining data accuracy and efficiency is a top priority in AI development. As an Data Quality Specialist, you will play a crucial role in ensuring the quality of our AI data.

Key Responsibilities:
  • Quality Audits: Perform thorough audits on annotated datasets to ensure they meet established guidelines and quality benchmarks.
  • Data Evaluation: Utilize metrics such as F1 score and inter-annotator agreement to evaluate data quality.
  • Error Analysis: Analyze annotation errors, trends, and project documentation to identify root causes and propose remediation strategies.
  • Edge-case Analysis: Resolve and analyze edge-case annotations to ensure quality and identify improvement areas.
  • Tool Proficiency: Efficiently use annotation and quality control tools to perform reviews and track quality metrics.
  • Project Expertise: Become an expert in project guidelines and provide feedback for clarifications or improvements.
  • Automation Opportunities: Identify opportunities for automation and contribute to the documentation of quality standards and procedures.
Requirements:
  • A minimum of 1 year of experience as a data analyst with exposure to data quality and/or data annotation, preferably in an AI/ML context.
  • Familiarity with basic concepts of AI/ML pipelines and data.
Preferred Skills:
  • Strong analytical and problem-solving skills with an exceptional attention to 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 thrive in a fast-paced environment with a focus on detail and strategic priorities.
  • Positive attitude and adaptability to new environments.
  • Confidence in speaking up when needed.
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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