Summary

As a Principal Scientist I within Preclinical Safety (PCS) Pathology Team, you will be responsible for advancing translational safety omics data analysis to support innovative drug discovery & development efforts. The candidate will participate in the study design, analysis, interpretation, and presentation of the data by interacting closely with a team of pathologists, bench scientists, and subject matter experts across the sites (e.g. USA, Switzerland) of Novartis. The role requires significant omics-based data science expertise and a passion to elevate the role of multimodal data analysis in translational drug safety.

About the Role

Your responsibilities include, but are not limited to:

• Use machine learning and statistical methods for analysis of spatial transcriptomics (e.g. Visium/Nanostring platforms) and spatial proteomics data (both internal and public data) from raw reads
• Compare and contrast spatial omics data sets with bulk RNASeq and single cell data
• Multimodal analysis of omics data with other modalities such as histopathology images, clinical biomarkers, etc.
• Support projects with data science expertise in diverse scientific fields such as gene and cell therapy, target discovery, genetics, drug safety, compound screening, etc.
• Innovate by transforming the way to solve a problem using Data Science & Artificial Intelligence
• Communicating regularly with stakeholders and assisting with answering their questions with the data and analytics
• Proactively evaluate the need of technology and novel scientific software, visualization tools and new approaches to computation to increase efficiency and quality of the Novartis data sciences approaches
• Independently identifies research articles and reproduce/apply methodology to Novartis business problems

• M.S. or PhD in Data Science, Computational Biology, Bioinformatics, or a related discipline
• Proficient in programming languages and data science workflows (e.g. Python, R, Git, UNIX command line, high-performance computing (HPC) clusters etc.)

• A minimum of 5 years of experience in analyzing large biological datasets (genomics, proteomics, and transcriptomics data analysis and data-integration) in a drug discovery/development or relevant academic setting
• Proven ability to implement exploratory data analysis and statistical inference in the context of scientific research
• A collaborative, team-focused mindset coupled with outstanding communication skills, and the ability to work in an agile environment
• Experience with using machine learning algorithms to extract insights from complex datasets
• Familiarity with the concepts of molecular biology, cell biology, genomics, biostatistics and toxicology