Clinical Data Labelling Specialist Oncology

Job Description

Our Client is looking for Clinical Data Labeling Specialist with a bioscience background to join our development team. This role is critical in supporting our oncology data pipeline by reviewing and labeling clinical data accurately for structured extraction, AI model training, and research initiatives. Candidates with prior experience in oncology data abstraction or data management roles in clinical trials are strongly preferred. Key Responsibilities: Review and label clinical oncology data from medical records, pathology reports, radiology findings, and molecular/genomic profiles. Ensure consistency, accuracy, and completeness in annotation across datasets used for model training and clinical validation. Apply standardized medical coding systems such as ICD-10, ICD-O-3, RxNorm, and HGVS for labeling and data harmonization. Work closely with medical reviewers, data engineers, and AI teams to refine labeling schemes and contribute to dataset quality control. Document and manage audit trails of labeling activities and highlight inconsistencies or data quality issues. Support ongoing data abstraction tasks for internal research and external oncology projects. Required Qualifications: Bachelors or Masters degree in Biosciences , Biotechnology, Life Sciences, or a related healthcare discipline. Familiarity with medical terminologies, particularly in oncology and related clinical domains. Ability to interpret unstructured clinical text and identify key data points (diagnosis, histology, tumor site, biomarkers, etc.). Preferred Qualifications: Experience in oncology data abstraction , working with clinical trials, cancer registries, or data management teams. Working knowledge and practical application of coding standards such as ICD- 10, ICD-O-3 (Oncology), RxNorm (medications), and HGVS (genetic variant notation) . Knowledge of clinical research workflows. Key Skills: Strong attention to detail and commitment to data accuracy. Analytical mindset with the ability to interpret complex clinical data. Effective communication and teamwork in a fast-paced development environment. Proficient in using Excel.