Principal Scientist - Pathology

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

WHY NOVARTIS

769 million lives were touched by Novartis medicines in 2020, and while we're proud of this, we know there is so much more we could do to help improve and extend people's lives.

We believe new insights, perspectives and ground-breaking solutions can be found at the intersection of medical science and digital innovation. That a diverse, equitable and inclusive environment inspires new ways of working.

We believe our potential can thrive and grow in an unbossed culture underpinned by integrity, curiosity and flexibility. And we can reinvent what's possible, when we collaborate with courage to aggressively and ambitiously tackle the world's toughest medical challenges. Because the greatest risk in life, is the risk of never trying! Imagine what you could do here at Novartis!

Commitment To Diversity & Inclusion

Novartis is committed to building an outstanding, inclusive work environment and diverse team's representative of the patients and communities we serve.

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Benefits and Rewards : Read our handbook to learn about all the ways we'll help you thrive personally and professionally : https : / / www.novartis.com / careers / benefits-rewards

Skills Required

Molecular Biology, biostatistics , Genomics, Cell Biology, toxicology , Machine Learning, Statistical Inference, Python, Machine Learning Algorithms, Git, Exploratory Data Analysis