About the role
AI summarisedJoin Apple's worldwide business development and strategy team as a Quality Analyst to contribute to the growth of a global initiative. This unique role focuses on ensuring the quality, reliability, and correctness of AI-powered and LLM-based features in real production scenarios, moving beyond traditional pass/fail testing.
TechnologyOnsiteMachine Learning and AI
Key Responsibilities
- Deep dive into financial and hierarchical data points across various hierarchies to understand complex data behavior.
- Define and implement test strategy based on Functional Requirements provided by the Product Manager.
- Collaborate with Product Managers to define test plans ensuring data quality for both new developments and regular data loads.
- Drive test coverage across source systems, transactional applications, and reporting environments, accounting for new features and regression.
- Execute test scenarios in a repeatable manner to facilitate easy data quality monitoring.
- Coordinate release management and hold the final go/no-go decision regarding data quality and functional behavior.
- Act as the first gate for Production Ops issues, confirming behavior and making priority calls with the Product Manager.
- Report findings in a clear, structured, and actionable manner to stakeholders.
- Collaborate with Engineers to understand implementation logic and manage tickets for data issues, planning bug fixes with the Product Manager.
Requirements
- Strong communicator capable of interpreting technical concepts and data findings for non-technical users.
- Proven 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/reporting tool (e.g., Tableau, Business Objects) to independently investigate datasets.
- Hands-on experience designing, implementing, and executing automated test cases using Python, JavaScript, or Selenium.
- Experience validating backend APIs, data pipelines, or service-based systems using automated approaches.
- Familiarity with LLM-based or AI-powered systems, including hands-on experimentation or professional exposure.
- Understanding of prompt-based systems and how input changes affect outputs.
- Experience working in cross-functional teams using Agile frameworks (Scrum or Kanban).
- Proficiency with collaboration and tracking tools such as Jira, Confluence, or similar.
- Strong problem-solving skills with meticulous attention to detail.