AI Data Quality Specialist - New Grad

Hire Feed; Italia; In loco; EUR 26.000 - 34.000; Tempo pieno

Descrizione del lavoro

Hire Feed is seeking a Human Data Manager, New Grad, to join as a full-time specialist responsible for maintaining high-quality datasets used in AI model training and evaluation.

You will review labeled data, resolve discrepancies, and help refine data pipelines and annotation processes, collaborating with cross-functional teams to raise data quality standards and support reliable machine learning outcomes.

Competenze

• Experience with data annotation, labeling, or quality assurance processes is preferred.
• Familiarity with basic data validation techniques and attention to detail in reviewing datasets.
• Ability to follow structured guidelines and maintain consistency in data handling tasks.
• Basic understanding of AI/ML concepts and their dependence on high-quality training data is beneficial.
• Proficiency in using spreadsheets or data management tools to track and organize information.

Mansioni

• Review and validate human-labeled data for AI training datasets.
• Identify and resolve discrepancies in data annotations by cross-referencing source materials and guidelines.
• Collaborate with cross-functional teams to refine annotation guidelines and improve data quality standards.
• Monitor and report on data labeling performance metrics to track progress and identify trends.
• Document processes and maintain records of data sources, labeling rules, and quality checks.

Conoscenze

Data annotation

Data validation

Guidelines compliance

AI/ML basics

Spreadsheet tools

Strumenti

Spreadsheet software

Descrizione del lavoro

Hire Feed is seeking a Human Data Manager, New Grad, to join as a full-time specialist responsible for maintaining high-quality datasets used in AI model training and evaluation.

You will review labeled data, resolve discrepancies, and help refine data pipelines and annotation processes, collaborating with cross-functional teams to raise data quality standards and support reliable machine learning outcomes.