ROLE SUMMARY
Work with Proteomics data to research, configure, optimize and test upstream data processing protocols for emerging experimental and analytical techniques. Additionally, the role will enable the integration and interpretation of large scale internal and external biological data sets. The successful candidate will have strong communication skills and a technical background allowing for facile interaction with computational biologists and experimentalists as tools are implemented.
ROLE RESPONSIBILITIES
* Research, configure, and optimize scientific algorithms for data analysis of various large-scale molecular data types
* Deliver analysis ready data sets with full statistical validation
* Integrate and curate internal and external data sets using standard identifiers from curated biomedical repositories
* Processing and analysis of complex, large-scale molecular data types unaddressed by current methods
* Maintain awareness of emerging methods in computational biology and applications for novel omics technologies
* Contribute innovative and novel scientific analyses to cross line platform projects
* Interface with Integrative Biology, Tech Center and Platform Line teams to determine needs and work with Business Technology to define solutions.
BASIC QUALIFICATIONS
* Post-graduate level training in Bioinformatics, Computational Biology or relevant natural sciences or computer sciences degree required
*BS with 8 years, MS with 6 years, or PhD with 3 years relevant experience applying quantitative approaches to solving biological problems, ideally in a pharmaceutical, biotech or comparable context.
PREFERRED QUALIFICATIONS
• Strong background in ‘omics pipeline development.
*Demonstrated experience in handling proteomics, NGS and other omics datasets
*Experience in analyzing “TMT-labeled” data sets
* Deep understanding of the statistical methods commonly used in omics analysis. Demonstrated experience applying computational and statistical approaches to deliver analysis result e.g. multivariate and machine learning approaches.
• Demonstrated ability to effectively interface with computational biologists to communicate and discuss results
• Exceptional communication skills (oral and written)