Data Scientist

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Description

1. Role Summary Oncoshot turns unstructured clinical documents (clinical notes, pathology, radiology and genomic reports) into a structured, research-grade record of each institution’s cancer population: diagnoses, histology, TNM and overall stage, biomarkers, treatments and disease status. Hospitals and research partners rely on that record only as far as its quality can be shown. The Data Scientist (Evaluation & Data Quality) owns the science of measuring it: how gold-standard data is sampled and annotated, how accuracy is estimated and compared between releases, how far model confidence can be trusted, and how complete and consistent the record is at each institution. This is a hands-on, statistics-driven role. The engineers build and ship the pipelines; the Data Scientist defines how quality is measured, what counts as a real improvement and where the bar sits. Patient-derived data never leaves the institution, so analyses run inside each hospital’s environment or on de-identified and aggregated results. To see how we measure extraction quality, look at our open-source evaluation framework, llmvalidate ([link] which this role works with and extends. 2. Key Responsibilities • Design the gold standard: Sample evaluation data stratified by document type, institution and rare but clinically important values, size each sample for the precision required, and decide when it must be refreshed. • Run annotation as a measurement proce

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