Clinical Scientist
Our mission is to hasten the transition to universally accessible healthcare. We are authorised by governments to assess and grant market access to medical AIs. Our groundbreaking approach enables the most innovative technology to reach patients safely and quickly.
Scarlet is the pre-eminent authority on AI medical devices. We serve customers that matter. Companies building bleeding-edge medical AI systems choose Scarlet. We are proud to count the world’s best resourced and most ambitious companies building medical AI as customers. You will be joining a team with product-market fit, flowing data, and exponentially growing revenue.
Come help us bring the next generation of healthcare to the people who need it.
About this role:
Scarlet’s Devices function is a team of clinicians, AI experts, and software engineers, working together to assess and certify the most innovative and impactful medical device software.
We pride ourselves on delivering fast and efficient assessments to enable market access, new device updates and ongoing surveillance of a growing portfolio of medical devices.
As we continue to scale our activities and certify more and more medical devices, we need clinical AI, real world evidence, and clinical research expertise to provide support across all stages of the customer journey.
You’ll join a team of clinical evaluation experts, conducting swift and accurate device assessments, whilst providing knowledge and expertise to internal functions and external stakeholders.
Your responsibilities:
Get authorised to assess the clinical evaluation processes of the most innovative medical devices in the world, focussing on clinical studies and clinical evidence
Collaborate with a multidisciplinary team on technical documentation assessments
Create content in areas like clinical AI, biostatistics, clinical research methodology, and real world evidence to educate teammates, customers and prospects
Deploy your expertise externally by representing Scarlet at events and conferences
Screen and action regulatory insights from the latest research, standards and guidance
Support the Sales and Customer Experience function by providing regulatory and scientific insights on prospects and customers
Work with our Product, Engineering, Design and Applied ML functions to build and improve our systems
Support the Technical Operations function as they expand Scarlet’s approvals in various jurisdictions and technologies
The key skills:
Education - A degree in clinical medicine, nursing, dentistry, epidemiology or public health
Work experience - Minimum of two years experience with the assessment of clinical data for medical devices
Work experience - Experience developing, implementing or evaluating medical device software
Work experience - Knowledge of the fundamental principles of the assessment of clinical data for medical devices and medical statistics
Work experience - Minimum of two years experience in patient care
Work experience - Practical experience in conducting or monitoring clinical investigations/trials or assessing clinical data
Desirable skills:
Ferociously curious - You like going down rabbit holes, understanding deeply how things work, and challenging the status quo
Excellent communicator - You have exceptional written & verbal communication skills
Highly adaptable - You have worked in different environments and like operating with autonomy on sometimes ambiguous tasks
Guidance knowledge - You have knowledge or training in the relevant standards and guidance used for clinical evaluation (e.g. ISO 14155, MEDDEV 2-7/1 Rev.4) [NBOG - clin eval]
Research savvy - You have knowledge of different clinical research methodologies, including experience designing or contributing to the development of interventional and non-interventional clinical studies
Biostatistics expert - You have experience either in the hands-on application of biostatistical methods in clinical research, or in the critical appraisal of statistical methodologies used in clinical studies and scientific literature
AI fluent - You have strong technical knowledge of modern generative AI (e.g., LLMs, embeddings, RAG, fine-tuning, evaluation/monitoring) and have a rich understanding of how these systems work - and fail