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Computational Biologist Job - #1547699A3

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Location: Durham, North Carolina, South United States, USA
Company: Syngenta
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Open Til: 22-Mar-12
Industry Sector: Agribusiness
Industry Type: Agronomy
Career Type: General
Job Type: Full Time
Minimum Years Experience Required: N/A
Salary: Competitive

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Division: SBI
Function: Information Systems
City: Durham
State/Province: North Carolina [NC]
Country: United States [US]
Position Title: Computational Biologist
Job ID: 2712

Role Purpose/Accountabilities:

- Support gene candidate discovery for plant traits and breeding by analyzing complex datasets in the context of biological pathways and phenotypes.
- Develop and apply statistical methodologies to analyze, integrate and extract value from expression, metabolite, and other omics datasets.
- Develop and apply computational approaches for generating biological hypotheses and gene candidates by connecting omics data and analyses to biological pathways.
- Work closely with experimental and computational biologists in research teams to design experiments, analyses, and validation efforts

Accountabilities:
- Keep current with the state-of-the-art in data integration and analysis methodologies.
- Implement cutting edge analytical methods and algorithms.
- Provide leadership on statistical applications for integrating complex datasets and extracting biological knowledge.
- Consult with diverse stakeholders on experimental design and analysis workflows.


Knowledge, Skills & Experience:

Critical knowledge:
- PhD or equivalent experience in Biostatistics, Computational Biology, Bioinformatics, Statistics or Applied Mathematics, preferably with a focus on biological problems.
- Thorough knowledge of the challenges associated with modern high throughput omics data, including sequence, expression, genomic variation and metabolic profiling data.

Critical experience:
- analyses in a life sciences setting, preferably involving omics data, metabolism, or plant biology.
- At least one year postdoctoral experience or equivalent preferred.
- Experience working effectively in multi-disciplinary teams.


Critical technical, professional and personal capabilities:
- Experience with gene expression analysis, gene co-expression networks and gene interaction networks.
- Fluency in a statistical programming language such as R (specifically Bioconductor) or SAS.
- Experience with expression analysis software such as Expressionist or JMP Genomics.
- Experience with pathway databases, analysis, and visualization tools such as Pathway Studio, Pathway Tools or Cytoscape.
- Excellent oral and written communication skills for both scientific and non-scientific audiences.


Critical leadership capabilities:
- Ability to influence the way biologists think about the problems they work on.
- Ability to integrate into a highly diverse team comprising multiple disciplines, nationalities, and cultural backgrounds.

Critical Success Factors & Key Challenges:
- Establish effective partnerships with experimental and computational biologists working in candidate gene discovery.
- Work productively in a team / matrix environment with geographically dispersed stakeholders.
- Identify and incorporate algorithms or methods from diverse scientific fields for application in plant lead discovery.
- Drive the integration and analysis of diverse and complex data for the generation of commercially relevant knowledge.


Additional Information:

- All applicants must be eligible to work in the US.
- Infrequent travel, including international travel, may be required.