BACKGROUND

RESPONSIBILITIES

• Apply statistical/machine learning algorithms to build compound sensitivity signatures from gene expression, DNA copy number, SNP or other high dimension datatsets, apply computational tools and interpret signature within a biological context.
• Actively seek and bring in cutting-edge tools and algorithms that help predictive signature analysis, results interpretation and decision making.
• Act as a singular resource of computational biology for scientific staff at multiple sites.
• Collaborate with software engineers to design and build up in-house computational biology infrastructure to facilitate data mining/analysis and results sharing.
• Collaborate with experimental groups to validate computational analysis; and communicate results to the scientific community through online reports and meeting presentations.
• Implement algorithms, tools, as necessary to drive drug discovery projects forward.

REQUIREMENTS

• PhD in computational or life sciences
• Minimum 7 yrs of relevant biotechnology or pharmaceutical industry experience
• In-depth knowledge about the role of computational biology in the drug discovery field
• Working knowledge of genomic and proteomics technologies, e.g., DNA copy number/SNP, gene expression, DNA-methylation and protein arrays
• Strong bioinformatics skills for data mining/integrating various biological datasets
• Hands-on experience of enterprise software development
• Expertise in the development and/or implementation of algorithms to distill, analyze and interpret complex datasets, particularly those associated with pharmacogenomics, pharmacogenetics and biomarker discovery
• Familiarity w/machine learning technologies
• Outstanding communication skills
• Customer-driven with ability to collaborate across multiple Research functions and geographic sites
• Fluent in at least one of the following statistical software programs: R, SAS, Matlab

HOW TO APPLY