BACKGROUND

RESPONSIBILITIES

• Contribute to the identification and validation of novel cancer targets leveraging public and proprietary cancer genomics and genetics data and work closely with target validation team.
• Represent bioinformatics discipline in drug discovery project team to provide broad range of support to ensure best use of all relevant data impacting project decisions:
  
  • Proactively identify opportunities and propose informatics solutions.
  • Independently carry out informatics analysis.
  • Summarize and present scientific results with clear conclusions and recommendations.
• Contribute to the development of innovative data management/integration and information intelligence solutions:
  
  • Act as bridge between end users and developers to clarify and define system requirement.
  • Provide bioinformatics support to ensure data quality and usability.
  • Conduct data mining to extract maximal value out of information system.
• Maintain expertise in state-of-the-art bioinformatics methods.
• Communication:
  
  • Proactively communicate to supervisor and key stakeholders of activities and status.
  • Generate patent, technical documents, external publications and presentation.
• Must be able to multi-task.

• Provide leadership within the bioinformatics team and act as scientific resources for team members.
• Propose and manage external collaboration opportunities.
• Manage projects and initiatives within the team or cross disciplines.
• Identify, evaluate, recommend and drive the adoption of new capabilities.

REQUIREMENTS

• Ph.D in bioinformatics, computational biology, computer science, or related field with 2+ years of working experience in related field.
• Proficiency in data analysis including:
  
  • Experience analyzing deep sequencing data.
  • Experience with diverse types of genomics data such as gene expression profiling, copy number profiling, RNAi screening, and expert user of genomics data analysis tools/packages such as Bioconductor, GenePattern, or equivalent.
  • Solid understanding of statistical analysis principles and expert user of one of the statistical data analysis tools/packages, such as R, matlab, or equivalent.
  • Experience and training in the area of machine learning.
  • Ability to independently manipulate large datasets in Unix/Linux environment.
• Excellent interpersonal skills with a proven record of interacting with biologists effectively.

ABOUT US

LOCALE

HOW TO APPLY