scarlet - Medical AI Scientist

• Education - A PhD in the area of AI in healthcare or Master’s (or higher) in Health Data Science or AI-focused medical statistics • Work experience - Experience evaluating AI/ML model performance metrics, validation methodologies, and training/testing datasets in healthcare or regulated environments • Work experience - Experience critically assessing whether statistical analyses and performance testing appropriately support clinical benefit and performance claims • Work experience - Experience communicating complex AI in healthcare topics to non-technical audiences • Research experience - You have experience of designing or contributing to the development of interventional and non-interventional clinical studies • Real world data - You have experience appraising or analysing real-world data and observational studies, including familiarity with bias frameworks (e.g. ROBINS-I, GRADE for observational evidence) • LLM-native - You have experience with the deployment and evaluation of LLMs in healthcare • Ferociously curious - You like going down rabbit holes, understanding deeply how things work, and challenging the status quo • Problem-solver - You can identify and define scientific problems, defend a scientific position and ideate pragmatic solutions • Highly adaptable - You have worked in different environments and like operating with autonomy on sometimes ambiguous tasks • THE INTERVIEW PROCESS • 1. Intro call with Sandy https://www.linkedin.com/in/wrightsandy/ - 30 mins • 2. Interview with Yun https://www.linkedin.com/in/yun-hsuan-chang-2b8a53138/ or Ed https://www.linkedin.com/in/edward-millgate-ph-d-98032799/ - 30 mins • 3. Technical interview with Mihir https://www.linkedin.com/in/dr-mihir-kelshiker-39968b57/ - 1 hour • 4. Culture and values interview with James https://www.linkedin.com/in/james-dewar-78758992/?originalSubdomain=uk and Jamie https://www.linkedin.com/in/jamie-cox-01458b3a/?originalSubdomain=uk - 2x30 mins • 5. Referencing & offer