Quality Lead

As an Annotation Quality Lead, you will play a crucial role in ensuring the accuracy and consistency of annotations within interdisciplinary and intradisciplinary datasets. Your responsibilities will include overseeing quality checks, performing intra- and inter-annotator quality control measures, and conducting regular audits to uphold standardized annotation practices. Collaborating closely with the Annotation Team, you will support ongoing quality assurance of annotation infrastructure and contribute to creating a Gold Standard database for quality benchmarking. Additionally, you will be responsible for conducting randomized checks to assess annotation performance, coordinating with the Annotation Program Liaison to review project objectives and quality standards, and defining appropriate sample sizes and quality control datasets in collaboration with scientists and engineers. Leading training sessions or competency evaluations to ensure adherence to annotation standards will also be part of your role. To be successful in this position, you must hold a Bachelors or Masters degree in Radiology, Medical Imaging, Pharmacy, Biomedical Engineering, or a related field, with a statistical background being a plus. Expertise in medical imaging modalities such as CT, MRI, X-ray, and ultrasound, as well as proficiency with medical annotation tools like ITK-SNAP, 3D Slicer, Labelbox, and V7 Labs, is essential. A strong understanding of clinical anatomy, pathology, and medical terminology, along with proven experience in medical image annotation and quality assurance, is required. Familiarity with DICOM, PACS, and other medical imaging systems is also important. Effective communication and collaboration skills are necessary for successful cross-functional teamwork, along with strong analytical skills and meticulous attention to detail. Knowledge of regulatory requirements, including HIPAA and GDPR compliance for handling medical data, is crucial in this role. You will report to the AI / Data Science Manager and collaborate closely with annotation specialists, scientists, and engineers across interdisciplinary teams. Experience with AI-driven medical image analysis models, deep learning techniques, automated annotation tools, or semi-automated pipelines would be considered a plus. Familiarity with clinical domains like oncology, cardiology, or pathology is also beneficial for this position.,