AI Data Labeling (Training/Evaluation) – McKinsey Forward Program
TextEngaged in structured evaluation and problem-solving tasks as part of the McKinsey Forward Program, leveraging analytical frameworks for consistent annotation decisions. Applied responsible AI concepts and human-centered technology for quality assessment of AI outputs. Utilized precision thinking, clear documentation, and attention to detail during simulated AI task evaluations. • Used McKinsey’s 7-step methodology to assess hypothetical labeling and annotation scenarios. • Practiced labeling rationale articulation and edge-case flagging using the Pyramid Principle. • Gained exposure to AI tools and responsible deployment practices relevant to human-in-the-loop reviews. • Participated in remote, self-directed work that mirrors professional annotation environments.