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.
Bachelor's degree or above in Life Sciences, Biomedical Sciences, Pharmacy, Bioinformatics, or a related field.
2+ years in life sciences content, drug R&D intelligence, clinical research, biomedical literature curation, or medical database work.
Able to read English biomedical literature and understand drugs, diseases, targets, biomarkers, clinical stages, and evidence levels.
Experience with DDTM, translational medicine, drug intelligence, clinical evidence, or biomedical databases preferred.
Detail-oriented and comfortable working through backlog while documenting repeatable rules.
Willing to use AI tools while owning the scientific/content judgment.
Chinese-English collaboration ability preferred.
