Principal Scientist - Pathology

As a Principal Scientist in the Preclinical Safety (PCS) Pathology Team at Novartis, you will play a crucial role in advancing translational safety omics data analysis to support innovative drug discovery and development efforts. Your responsibilities will involve collaborating with pathologists, bench scientists, and subject matter experts across different sites such as the USA and Switzerland. Your expertise in omics-based data science and enthusiasm for enhancing multimodal data analysis in translational drug safety will be key in this role. Key Responsibilities: - Utilize machine learning and statistical methods to analyze spatial transcriptomics (e.g. Visium/Nanostring platforms) and spatial proteomics data from raw reads - Compare and contrast spatial omics data sets with bulk RNASeq and single-cell data - Conduct multimodal analysis of omics data with other modalities like histopathology images and clinical biomarkers - Provide data science expertise for projects in various scientific fields such as gene and cell therapy, target discovery, genetics, drug safety, and compound screening - Innovate problem-solving approaches using Data Science & Artificial Intelligence - Communicate regularly with stakeholders, addressing their queries with data and analytics - Evaluate the need for technology, scientific software, visualization tools, and computation approaches to enhance Novartis data sciences efficiency and quality - Independently identify research articles and apply methodologies to solve Novartis business problems Qualifications Required: - M.S. or Ph.D. in Data Science, Computational Biology, Bioinformatics, or a related discipline - Proficiency in programming languages and data science workflows like Python, R, Git, UNIX command line, and high-performance computing clusters - Minimum of 5 years of experience in analyzing large biological datasets in a drug discovery/development or academic setting - Ability to implement exploratory data analysis and statistical inference in scientific research - Collaborative mindset, excellent communication skills, and agility to work in a team environment - Experience with machine learning algorithms for insights extraction from complex datasets - Familiarity with molecular biology, cell biology, genomics, biostatistics, and toxicology concepts,