Job Title

This role requires a deep understanding of computational methods for analyzing sequencing-based perturbation data (e.g. Perturb-seq, CROP-Seq), a passion for innovation, and a commitment to improving healthcare outcomes through cutting-edge technology.We are looking for exceptional researchers with a passion for interdisciplinary research and technical problem-solving, and a proven ability to develop and implement research ideas. The candidate is expected to analyze genome-scale perturbation data, as part of the high-profile FDFM Initiative, in collaboration with other computational scientists within gRED Computational Sciences (gCS), and our therapeutic area leads.This data will be used to derive early insights for our Oncology portfolio, and will also feed the large-scale training of Perturbation Foundation Models, enabling a paradigm shift towards Lab-in-the-Loop exploration of perturbative biology. The candidate is also expected to contribute methods and software to a large community of computational scientists across gCS aiming to derive insights from large-scale perturbation datasets.Key Responsibilities Analyze large-scale high-content perturbation datasets Collaborate with interdisciplinary and cross-functional teams including biologists, chemists, data scientists, and other stakeholders. Work with computational leaders in gCS to derive insights for our oncology portfolio. Contribute to the broader scientific community through contributions to our computational pipeline.Qualifications Educational Background: PhD degree in quantitative field (e.g., Computational Biology, Bioinformatics, Computer Science, Statistics, Mathematics) or in the physical or life sciences (e.g., Chemistry, Biology) with a strong quantitative focus. Experience:o Proven track record of analyzing large-scale Perturb-seq/CROP-seq datasetso Demonstrated interest in problems across biology and chemistry as applied to the discovery and development of treatments for disease. Technical Skills:o Proficiency in scientific programming in Python.o Strong background in statistics, probabilistic modeling and data analysis. Soft Skills: Excellent communication, collaboration, and problem-solving skills. Publications: Strong publication record and experience contributing to research communities.Preferred Multimodal data integration, in particular between multiple measurement modalities and/or clinical patient dataPlease ensure you include github/public presentation of the candidate in the resumeThis role is a remote role however must reside in Canada with the legal , in Canada