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Expert/Sr. Expert, Data Science – Immuno-Oncology & Hematology (347958BR)

Science and Research

Food Science

Yearly

No

Cambridge, Massachusetts, United States

The Novartis Institutes for BioMedical Research (NIBR) is Novartis’ innovation engine, focusing on powerful new technologies that have the potential to help produce therapeutic breakthroughs for patients. Within NIBR, the Immuno-Oncology & Hematology (IOH) department is dedicated to developing and deliver cutting-edge therapies to benefit patients with cancer and blood disorders. <br><br>We are seeking an experienced data scientist/computational biologist/bioinformatician to join the IOH Data Science team to support the drug discovery process. In this very collaborative environment, you will engage in a range of exploratory and translational research activities and will support target and biomarker discovery through data curation, visualization, and computational analysis. You will contribute computational expertise to the department, work closely with IOH project teams and collaborate with other NIBR data scientists.<br><br>The ideal candidate should be a creative, proactive, and flexible scientist, a highly motivated and open-minded team player that communicates clearly and effectively, and a continuous learner. You will help to answer biological questions via computational analyses that leads to interpretable results. As a result, you need to have a solid understanding of omics data, data science and machine learning methodologies.<br><br>This is a unique opportunity to contribute to drug development to deliver innovative new therapies to patients with cancer and blood disorders. <br> <br>Responsibilities include, but not limited to: <br><br>•Interrogate large-scale omics datasets, including single-cell data, to answer research questions and drive target and biomarker discovery. <br>•Contribute to the IOH Data Science team’s key activities on immuno-oncology.<br>•Help build computational infrastructure that facilitates sustainable development of computational tools, storage and integration of large datasets, and simultaneous query of multiple datasets. <br>•Make oral and written updates in team meetings as needed, communicating key findings to both computational and experimental biologists. <br>•Document analysis code and results in electronic lab notebooks and contribute to scientific publications. <br>•Keep abreast of newly published methods in machine learning and computational biology. <br><br>[#video#https://youtu.be/B88bPEKkDI4{#400,300#}#/video#]