**Role & seniority: ** QA Analyst (Quality Assurance Analyst), mid-level (3–5 years)

**Stack/tools: **

QA/testing methodologies & tools
Dataset validation/auditing/error detection practices
Spreadsheets, databases, reporting tools
Basic AI/ML workflow knowledge (advantage)
Top 3 responsibilities:
- Review and validate datasets, AI outputs, and operational/software results for accuracy and consistency
- Identify/report errors, inconsistencies, and quality gaps; verify deliverables against standards
- Collaborate with Data Ops/AI teams to implement quality improvements; maintain QA documentation
Must-have skills:
- Strong QA testing fundamentals (quality control, protocols, documentation)
- Dataset quality practices: validation, auditing, error detection
- Proficiency with spreadsheets, databases, and reporting
- Compliance with project guidelines and ethical requirements
- Degree in CS/Data/Statistics/QA-related field; QA testing/data quality familiarity
Nice-to-haves:
- Basic AI/ML workflow knowledge
- QA/data-related certifications (e.g., QA Testing; Data Quality/Analytics)
Location & work type: Not specified in provided text (QA Analyst role).

Full Description

QA Analyst (Quality Assurance Analyst)

Job Summary: We are looking for a QA Analyst ensures the quality, consistency, and reliability of datasets, AI outputs, digital content, and operational workflows. This role evaluates annotated datasets, AI model responses, software outputs, and content deliverables to ensure they meet defined standards and project requirements. The QA Analyst will help to maintain high data integrity, reducing errors and improving AI system reliability.

Key Responsibilities

Review datasets, AI outputs, and operational results for accuracy. Identify and report inconsistencies, errors, and quality gaps. Collaborate with data operations and AI teams to implement quality improvements. Maintain QA documentation and quality control processes. Develop and apply testing protocols for dataset annotation and AI output evaluation. Ensure compliance with project guidelines, standards, and ethical requirements.

Job Requirements

Bachelor’s Degree in Computer Science, Data Science, Information Systems, Statistics, Quality Assurance or related fields Familiarity with QA testing methodologies and tools. Understanding of dataset validation, auditing, and error detection. Experience with spreadsheets, databases, and reporting tools. Basic knowledge of AI or ML workflows (advantage). Required Certifications such as Quality Assurance or QA Testing Certification, Data Quality/Analytics Certification (advantage) 3-5 years of proven experience in QA, data validation, or analytics.