Senior Computational & Applied AI - Scientist

Roche’s Research and Early Development division creates transformative medicines, and its Computational Sciences Centre of Excellence supports these efforts by making the organization more data‑driven and digitally capable. Within the Computational Medicine unit, the Imaging Data Insights team—spread across Basel, Penzberg and South San Francisco—seeks senior scientists who can elevate imaging data analysis throughout the clinical pipeline. In this position you will work closely with disease‑area experts, clinical scientists and biomarker groups to turn scientific questions into computational strategies. Your daily work will involve designing and applying cutting‑edge AI and machine‑learning methods, including generative and agentic models, to biomedical images and multimodal datasets. You will process imaging data from both preclinical studies and clinical trials to inform study design, patient stratification, biomarker discovery and portfolio decisions, while building and documenting reusable analysis pipelines that meet global standards. Acting as a trusted partner, you will communicate results across the enterprise and mentor junior colleagues, fostering an inclusive and collaborative culture. The role requires a PhD—or an equivalent qualification—in a scientific or technical field such as computer science, biomedical engineering, applied mathematics or physics, together with at least three years of experience in pharmaceutical research and development. Candidates must possess deep expertise in biomedical image analysis, data science and AI/ML, and have a proven track record of creating computational workflows that translate complex imaging and biological information into actionable insights. Beyond the core qualifications, the position offers the chance to work in a globally connected team that values curiosity, responsibility and humility. You will be part of a network of imaging specialists and computational biologists, contributing to a culture that emphasizes psychological safety, continuous learning and the collective impact of data‑driven discoveries on patient outcomes.