• Process DDTM/translational medicine literature screening, entity extraction, relationship judgment, evidence capture, and field completion.
• Evaluate relationships among drugs, diseases, targets, biomarkers, clinical evidence, and translational evidence.
• Create positive examples, negative examples, edge cases, and historical error samples for AI workflow evaluation.
• Help define DDTM fields, quality thresholds, review rules, and migration acceptance criteria.
• Partner with the AI Native Data Engineer to convert manual decisions into Skills, prompts, rules, QA checklists, and error loops.