TaskAtlas: Discovering Data-Analysis Task Spaces from a Simulated Analyst Society

Constructing data-analysis benchmarks requires discovering real-world analytical tasks. Existing efforts typically rely on expert-led task-space construction, which requires continued human effort and may underrepresent analytical tasks across diverse occupations. To address these issues, we introduce TaskAtlas, a framework for discovering data-analysis task spaces from an occupationally anchored simulated analyst society. TaskAtlas selects occupations relevant to the target analytical domain from public occupational records and uses their profiles to construct simulated analysts with diverse backgrounds and work contexts. It then employs an interviewer agent to elicit their analytical needs through multi-turn conversations, extracts tasks from these needs, and organizes them into an explicit task space. We evaluate TaskAtlas in tabular text analysis and causal inference, examining the authenticity of elicited needs and the coverage of the discovered task space. The results show that the elicited needs receive external support from real world and the task space recover established tasks in expert-curated taxonomy. More importantly, TaskAtlas enables new task formulations to emerge, including text augmentation for predictive data analysis (TA++) and latent-confounder detection (LCD), offering new directions for data-analysis research.