Sponsor: Rubicon Genomics
Recording Date: 2/19/2014
Recording Time: 1 hour
Novartis is conducting comprehensive genomic screening of patients enrolled in ongoing early- and late-stage oncology clinical trials, with the aim of identifying the best genetic predictors of drug efficacy for individual patients. The Translational Informatics group within the Oncology Translational Medicine department at Novartis is seeking a highly motivated bioinformatics scientist who will accelerate our efforts to harness sequencing and other genetic profiling technologies toward the selection of personalized cancer therapies.
The successful applicant will conduct analyses of genomic data (e.g. mutation, copy number, expression, phosphorylation) from clinical trial patients and pre-clinical models, assayed internally and in collaboration with academic and industrial partners. Responsibilities include the following activities:
1. Support individual project teams with specific analyses of patient genetics data, including prioritization of patient selection biomarkers in key genes and pathways, identification of biomarkers and combinations correlated with drug response, comparison of findings with data from independent sources (e.g. Cancer Cell Line Encyclopedia, TCGA);
2. Generate informative visualizations;
3. Build and support streamlined analysis processes and modular pipelines;
4. Interact with internal and external scientific collaborators;
5. Work with software developers to guide the development of secure databases for patient data.
In addition to these principal responsibilities, the successful applicant will be engaged with the scientific community within Novartis and in external collaborations, both to stay abreast of innovations and to contribute to scientific projects.
1. Ph.D. in Bioinformatics, Genomics, Computer Science, Systems Biology or equivalent.
2. Working knowledge of current DNA, RNA and protein profiling technologies, and proficiency with relevant analysis tools.
3. Knowledge of cancer biology and familiarity with the literature (e.g. TCGA articles).
4. Experience mining common genomics databases and integrating various types of genomic data.
5. Proficiency in Linux on a high-performance cluster and coding in a common programming language such as Perl, Python, R or Java; adherence to good coding practices.
6. The ability to communicate well, both verbally and in writing.
7. Excellent organizational skills and motivation.
1. Knowledge of statistics and predictive modeling.
2. Experience with pathway analysis tools, prototyping visualizations.
3. Expertise in a specialized area of cancer biology, such as immunology or epigenetics.