Job Title : Clinical Data Specialist
Duration : 6 months
Location : Sunnyvale, CA
Department: IntraOp Intelligence
Reports to: Manager, Data Analyst
Company Description:
- Client designs and manufactures state-of-the-art robot-assisted systems for use in minimally-invasive surgery. These systems are revolutionizing the way in which surgery is being done and offer a unique platform—that is being used routinely at hospitals worldwide—for exploring the potential of intelligent surgery. Joining Client means joining a team dedicated to using technology to benefit patients by improving surgical efficacy and decreasing surgical invasiveness, with patient safety as our highest priority.
Primary Function:
- We are seeking a highly motivated Clinical Data Specialist to support the development and validation of artificial intelligence and machine learning technologies for robotic-assisted surgery.
- The Clinical Data Specialist will create, review, and maintain high-quality clinical ground truth datasets derived from surgical videos, images, and associated clinical information. This role operates at the intersection of clinical knowledge, data operations, and product development, helping translate clinical and technical requirements into accurate, consistent, and traceable datasets.
- The successful candidate will perform clinical annotation and quality review, investigate annotation discrepancies, maintain dataset and methodology documentation, and support the preparation of datasets used for machine learning research, product development, and verification and validation activities.
- This individual will collaborate closely with machine learning engineers, researchers, surgeons, clinical experts, quality and regulatory partners, data operations teams, and external annotation providers. The ideal candidate combines strong knowledge of anatomy and surgical workflows with exceptional attention to detail, documentation discipline, and sound judgment when working with complex or ambiguous clinical data.
Responsibilities:
- Review annotations produced by human annotators, clinical experts, external providers, and AI/ML models to ensure accuracy and consistency.
- Identify, document, and escalate ambiguous cases, annotation discrepancies, and data-quality risks for clinical review or adjudication.
- Participate in annotation training, calibration, qualification, and inter-annotator agreement activities.
- Maintain annotation guidelines, quality criteria, review decisions, dataset changes, and other supporting documentation.
- Support traceability between source data, annotation versions, quality reviews, adjudic