About the role
AI summarisedApple is seeking a Quality Analyst to join its worldwide business development and strategy team, focusing on validating the quality, reliability, and correctness of AI-powered and LLM-based features in production scenarios. The role involves deep data analysis, test strategy definition, and collaboration with product managers and engineers to ensure data quality for strategic initiatives.
TechnologyFull-timeMachine Learning and AI
Key Responsibilities
- Deep dive into various financial & hierarchical data points in different sets of hierarchies, understanding the nuances of how complex data behaves
- Define and implement the test strategy based on Functional Requirements driven by the Product Manager
- Work closely with the Product Managers to define test plans that ensure data quality both on new development and regular data loads
- Drive test coverage across different source systems, transactional applications and reporting environments, taking into account new features and regression
- Execute test scenarios in a repeatable manner, allowing for easy data quality monitoring
- Coordinate release management and hold the final go / no-go in terms of data quality and functional behaviour
- Act as first gate for Production Ops issues, confirm the behavior and makes the call on priority with Product Manager
- Report findings in a clear, structured, and actionable manner
- Collaborate with Engineers to understand implementation logic
- Manage tickets on found data issues and work with the Product Manager to plan out bug fixes
- Communicating status updates to end users and stakeholders concisely in a timely manner
Requirements
- Strong communicator with the ability to interpret technical concepts and data findings for non-technical end users
- Experience applying a data-driven QA approach, including data reconciliation or validation, in business-critical environments
- Hands-on experience with SQL and at least one analytics or reporting tool (e.g., Business Objects, Tableau, or similar), with the ability to independently investigate and validate datasets
- Hands-on experience designing, implementing, and executing automated test cases using languages or frameworks such as Python, JavaScript, or Selenium
- Experience validating backend APIs, data pipelines, or service-based systems using automated approaches
- Experience working in cross-functional teams using Agile frameworks such as Scrum or Kanban
- Familiarity with LLM-based or AI-powered systems, including hands-on experimentation or professional exposure
- Understanding of prompt-based systems and how changes to prompts or inputs affect outputs
- Experience using collaboration and tracking tools such as Jira, Confluence, Quip, or similar
- Strong problem-solving skills with attention to detail
- Self-motivated, proactive, and able to work independently or as part of a team
- Ability to learn quickly and adapt in a fast-paced environment
- Professional experience testing LLM-based systems (e.g., chat, summarization, classification, extraction, or RAG workflows)
- Experience building or maintaining scalable automated test frameworks beyond test case execution
- Strong proficiency in SQL, including complex joins, aggregations, and large dataset validation
- Understanding of Dimensional Modeling and Data Warehousing concepts
- Experience testing data migration projects, including source-to-target validation
- Familiarity with CI/CD pipelines and integrating automated tests into release workflows