Clinical AI Data Specialist

Requirements:

• Background in medicine, clinical research, life sciences, pharmacy, oncology data, clinical informatics, RWE, clinical data abstraction, or healthcare data operations.
• 3-7 years of relevant experience in clinical data curation, RWE, clinical research, oncology abstraction, registry abstraction, trial screening, clinical data management, or healthcare data operations.
• Ability to read and interpret complex clinical documents and translate them into precise annotation rules.
• Strong written communication; ability to write guidelines with examples, counterexamples, edge cases, and clear decision logic.
• Basic programming/data skills: Python basics, pandas/CSV/JSON handling, SQL querying, and Excel/Google Sheets for review, QA, and project tracking.
• GenAI literacy: ability to use LLM tools for drafting, pre-labeling, review, summarisation, consistency checks, and data workflows while understanding their limitations.
• Basic ML literacy: labels, training data, validation/test sets, precision/recall, overfitting, leakage, model evaluation, and why labels must be measurable and consistent.
• Strong operational discipline for managing annotation projects, reviewer feedback loops, and versioned guideline updates.
• Ability to work cross-functionally with clinicians, annotators, ML engineers, evaluation engineers, and project stakeholders.