Data Quality Analyst – AI/ML

Innodata India Private Limited; Thoothukudi; On-site; INR 500,000 - 750,000; Full time

Job summary

Innodata India Private Limited is seeking a Data Quality Analyst - AI/ML to ensure the accuracy, consistency, completeness, and reliability of datasets used in AI/ML applications. The role involves reviewing large volumes of digital content and datasets, identifying quality issues, applying defined quality standards, and supporting continuous improvement of AI data workflows.

You will perform data validation, quality checks, and audits, track quality metrics, and collaborate with AI/ML, Data

Qualifications

• 1-4 years of experience in Data Quality, Data Validation, QA, Content Review, Data Ops or AI/ML operations.
• Experience reviewing text, images, videos, or digital content.
• Strong attention to detail and ability to follow detailed guidelines.

Responsibilities

• Review and evaluate text, images, videos, audio, and other digital content against quality standards.
• Identify inaccuracies, inconsistencies, missing information, duplicates, and other quality issues.
• Perform data validation, quality checks, and audits across large datasets.
• Apply quality guidelines, classification rules, and evaluation criteria consistently.
• Investigate recurring quality issues and identify root causes.
• Maintain high levels of accuracy, consistency, and productivity on large data volumes.
• Conduct sample-based quality audits and report findings.
• Provide feedback to internal teams to improve data quality.
• Track quality metrics and report trends and issues.
• Collaborate with AI/ML, Data Ops, Content, and Product teams.
• Support development and improvement of quality-control processes and SOPs.
• Ensure deliverables meet client and project requirements.

Skills

Data quality

Attention to detail

Analytical thinking

Communication

Tools

MS Excel

Google Sheets

SQL basics

Job description

Job Summary

We are looking for a Data Quality Analyst - AI/ML to ensure the accuracy, consistency, completeness, and reliability of datasets used in AI/ML applications. The role involves reviewing large volumes of digital content and datasets, identifying quality issues, applying defined quality standards, and supporting continuous improvement of AI data workflows.

Key Responsibilities

• Review and evaluate text, images, videos, audio, and other digital content against defined quality standards.
• Identify inaccuracies, inconsistencies, missing information, duplicates, and other quality issues.
• Perform data validation, quality checks, and audits across large datasets.
• Apply project-specific quality guidelines, classification rules, and evaluation criteria consistently.
• Investigate recurring quality issues and identify potential root causes.
• Maintain high levels of accuracy, consistency, and productivity while working with large volumes of data.
• Conduct sample-based quality audits and report findings.
• Provide detailed feedback to internal teams to improve overall data quality.
• Track quality metrics and prepare reports on identified trends and issues.
• Collaborate with AI/ML, Data Operations, Content, and Product teams.
• Support the development and improvement of quality-control processes and SOPs.
• Ensure deliverables meet defined client and project requirements.

Required Skills

• 1-4 years of experience in Data Quality, Data Validation, Quality Assurance, Content Review, Data Operations, or AI/ML-related operations.
• Strong attention to detail and ability to identify errors in large datasets.
• Experience reviewing text, images, videos, or digital content.
• Strong analytical and problem-solving abilities.
• Ability to understand and follow detailed guidelines.
• Good communication and documentation skills.
• Proficiency in MS Excel / Google Sheets.
• Comfortable working with repetitive, detail-oriented tasks while maintaining high accuracy.

Preferred Skills

• Experience working on AI/ML, Generative AI, Computer Vision, NLP, or digital content projects.
• Experience with data quality or content quality platforms.
• Basic knowledge of SQL, Python, or data analysis is an advantage.
• Experience with quality auditing, content review, or digital operations.