Clinical Data Specialist

Clinical Data Specialist

Duration: 6 months

Location: Sunnyvale, CA / Remote

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, adjudication decisions, and approved dataset outputs.
• Collaborate with engineers, researchers, surgeons, clinical experts, and external providers to translate clinical data requirements into annotation and review activities.
• Contribute to clinical data collection, tool and model evaluation, and workflow improvements that increase efficiency while maintaining clinical accuracy and data quality.
• Conduct clinical research through literature review, procedural analysis, and consultation with clinical experts to support AI/ML research and product-development needs.

Technical Requirements

• Bachelor's degree in biology, physiology, biomedical engineering, nursing, clinical sciences, health sciences, or a related field.
• Strong understanding of human anatomy and an interest in learning surgical procedures across multiple specialties.
• Experience interpreting medical images, surgical videos, clinical workflows, or other complex visual data.
• Experience with data annotation, clinical data review, medical research, clinical operations, or a related field.
• Ability to learn and use specialized annotation platforms, data-management systems, and project-tracking tools.
• Strong analytical and problem-solving skills, including the ability to recognize patterns, inconsistencies, and potential data-quality risks.
• Demonstrated ability to follow detailed clinical definitions and apply them consistently.
• Excellent documentation skills and the ability to clearly explain annotation decisions and clinical reasoning.
• Ability to handle confidential, proprietary, and sensitive clinical information professionally.

Soft Skills and Leadership Requirements

• Excellent attention to detail and commitment to clinical accuracy and data quality.
• Strong written and verbal communication skills, including clear documentation of decisions and concerns.
• Ability to collaborate effectively with clinical, technical, operational, and quality-focused stakeholders.
• Sound judgment regarding when to make an independent decision and when to escalate a clinical or quality concern.
• Ability to translate clinical and AI/ML research objectives into practical data collection, annotation, and quality-review guidelines.
• Ability to manage multiple projects, priorities, and deadlines in a fast-paced environment.
• Proactive and transparent in communicating mistakes, uncertainty, risks, and process challenges.
• Ability to work independently while remaining closely coordinated with a cross-functional team.
• Open to giving and receiving constructive feedback and contributing to continuous improvement.