Requirements Analysis:

• Collaborate with Product Managers and Engineers to validate requirements and identify edge cases early in the development cycle.
• Convert user stories and feature specifications into clear, testable acceptance criteria.

Data Quality & Consistency:

• Validate data accuracy, consistency, and conflict handling across multiple sources (inputs, outputs, model predictions).
• Identify data quality issues and propose improvements for more resilient systems.

AI/ML Quality Validation:

• Test AI/ML features for accuracy, bias, stability, and edge-case behavior.
• Evaluate the reliability and correctness of automation logic and model responses.

Defect Reporting & Quality Tracking:

• Document defects with clear steps, impact assessment, and risk evaluation.
• Track and communicate QA metrics, risks, and overall quality status to stakeholders.

Cross-Functional Collaboration:

• Work closely with Developers, Engineers, and Product teams to align on quality goals and SLAs.
• Communicate issues, risks, and improvements effectively and constructively.