Job Description:
Monsanto seeks a creative Computational Biologist to help transform unprecedented *omics data into higher yielding crops. You will participate in the design of biological and computational experiments across several projects, generate biologically meaningful and testable hypotheses, and deliver robust analyses to other team members and internal customers. You will also develop, optimize, and share bioinformatics tools with the larger Monsanto scientific community.
Ideal candidate will have PhD degree in a quantitative biology discipline (e.g. computational biology, bioinformatics, biophysics, biostatistics) or a degree in biology or in a quantitative/computational discipline with significant experience in the complementary domain. Demonstrated success in working with multi-disciplinary research team requiring scientific creativity, innovative thinking, organization and collaborative skills are necessary for this role.
Role and responsibility
• Generate hypotheses using computational methods to integrate and interpret data from various sources such as transcriptomic, genomic, metabolomic, field and molecular assays
• Generate intellectual property from large amount of genomic and field data using sound statistical methods
• Develop systems biology approaches to identify trait-gene or trait-marker associations
• Work with other research teams to continually improve the bioinformatics capabilities and workflows associated with next generation sequencing to support population and comparative genomics projects.
Qualifications:
• Ph.D. in a Computational, Statistical, Biophysics, or Bioinformatics related field.
• Strong publication record in the field of Genetics, Bioinformatics, or Statistics.
• Demonstrated successful development of novel, statistically-motivated algorithms to extract meaning from diverse biological data.
• Successful experience working closely with experimental collaborators to test computationally derived hypotheses.
• Ability to work on cross-functional teams to meet milestones and deadlines.
• Excellent verbal and written communication skills.
• Facility with the programmatic use of at least one broadly accepted statistical package (e.g. R, SAS, S+, Minitab).
• Scientific programming skills in at least one high performance (e.g. C, C++, Java, Scala) and one scripting language (e.g. Perl, Python).
• Ability to write SQL queries and design simple relational database schemas.
• Demonstrated ability to learn methods and technologies as necessary to meet objectives.
Desired Skills/Experience:
• 2 years industry or postdoc experience in computational biology.
• Past laboratory experience a plus, along with an appreciation for when to use a computational approach and when to do an experiment.
• Ability to use high performance computing environments. Parallel/concurrent programming ability is a plus.
• Experience guiding experimentalists on the best use of computational approaches for their projects and guiding informaticists on how to best meet the requirements of experimentalists.
• A highly collaborative approach, with the drive and ability to actively create new connections across the Monsanto Technology organization.
Qualifications:
• Ph.D. in a Computational, Statistical, Biophysics, or Bioinformatics related field.
• Strong publication record in the field of Genetics, Bioinformatics, or Statistics.
• Demonstrated successful development of novel, statistically-motivated algorithms to extract meaning from diverse biological data.
• Successful experience working closely with experimental collaborators to test computationally derived hypotheses.
• Ability to work on cross-functional teams to meet milestones and deadlines.
• Excellent verbal and written communication skills.
• Facility with the programmatic use of at least one broadly accepted statistical package (e.g. R, SAS, S+, Minitab).
• Scientific programming skills in at least one high performance (e.g. C, C++, Java, Scala) and one scripting language (e.g. Perl, Python).
• Ability to write SQL queries and design simple relational database schemas.
• Demonstrated ability to learn methods and technologies as necessary to meet objectives.
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