Novartis NIBR Computational Biology Data Scientist Vacancy 2021

Novartis NIBR Computational Biology Data Scientist Vacancy 2021

The NIBR research site in San Diego, CA is seeking a computational biology data scientist with extensive experience analyzing Omics data (i.e., bulk and single cell RNAseq, spatial transcriptomic, epi-genomic, proteomic, genetic) to support drug discovery research in Regenerative Medicine. You’ll work at the cutting edge of data science to solve important challenges by applying your technical abilities for curating, integrating, and mining complex biological data. You’ll work closely with a multicultural and multidisciplinary team composed of biologists, chemists, and high throughput screening scientists to develop complex human cellular models that will be used to identify molecules that promote healthy tissue regeneration. The successful candidate will be highly motivated, creative and collaborative, and will possess effective communication skills.

Job Title: Computational Biology Data Scientist Novartis NIBR San Diego

Job ID: 334682BR

Minimum requirements

What you will bring to the role:

• Ph.D. in computational biology, bioinformatics, biomedical engineering, computer science, or related field (alternatively, MSc plus equivalent life science industry experience).

• Extensive knowledge and experience in the analysis of bulk and single-cell RNAseq data sets is required.

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• Experience analyzing and integrating multi-Omic data sets (e.g., transcriptomics, proteomics, epi-genomics, proteomics) is highly desired.

• Significant experience in R/Python, literate programming notebooks, git, high-performance computing and competency working in a Linux/Unix environment is required. Proficiency in other languages, e.g., SQL, Java, Matlab is a plus.

• Demonstrated experience with data engineering, machine learning, and AI for studying biological problems, ideally for omics data.

Responsibilities will include:

• Develop analytical workflows for the integration and mining of large collections of single-cell RNASeq and other Omic data sets

• Collaborate with teams developing human cell-based models of disease, based on key transitional cell types defined by analysis of integrated multi-omic data sets.

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• Contribute to drug discovery efforts by designing and analyzing Omic data sets derived from novel human cell models for the identification of molecules that promote healthy tissue regeneration.

• Clearly communicating key findings through interactive data visualizations and presentations, and the development of facile data mining platforms (e.g., Spotfire, R Shiny).

Novartis NIBR Computational Biology

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