Function: Trait Technology
State/Province: North Carolina [NC]
Country: United States [US]
Position Title: Research Scientist 1 - data manager
Job ID: 3448
- This statistician / data manager position will support biotechnology research and development by applying statistical methods to experimental design, process optimization, data analysis and report preparation; work closely with laboratory, greenhouse and field scientists to optimize experimental processes and conditions; consult with computational biologists on methodologies and best practices for complex data analysis; provide formal and informal training to scientific colleagues on basic statistical techniques; work with software programmers to develop tools for scientists to be used for data analysis; and evaluate and recommend new statistical methodologies, software and data management solutions.
- Keep current with the state-of-the-art in statistical analysis, experimental design and process optimization methodologies Implement cutting edge statistical methods to advance research goals for complex traits Provide leadership on statistical applications and tools Consult with diverse stakeholders on workflows, experimental design, process optimization, data analysis, report preparation, and data archival Analyze and interpret results and write and communicate final reports Maintain accurate experimental records according to company policies with outstanding attention to details Invent innovative process and products and protect novel findings through submission of invention disclosures Perform all activities in accordance with applicable Syngenta Health, Safety, and Environmental (HSE) policies
Knowledge, Skills & Experience:
Critical knowledge: :
- Ph.D. or M.S. degree in statistics, biometrician or related field, preferably with a focus on plant biology thorough knowledge in experimental design, data analysis, process optimization, and data management Knowledge and experience in plant biology, breeding and genetics
- A minimum of 3-year of experience in applied statistical methods in a life sciences setting Experience in experimental design to understand plant performance under experimental conditions in lab, greenhouse and field environments Experience working effectively in multi-disciplinary teams Experience with training or mentoring scientific colleagues on statistics is preferred
Critical , technical , professional & personal capabilities:
- Experience in designing complex experiments related to plant physiology, breeding, agronomy and molecular biology Experience in analyzing complex biological datasets Experience with linear, non-linear, longitudinal, and general linear models, power calculations, and non-parametric methods Proficient in a statistical programming language such as R or SAS Demonstrated ability to communicate statistical ideas effectively to non-statisticians Proficiency in use of standard Microsoft office software Demonstrated experience in working effectively as a member of a dynamic technical team and ability to communicate across multiple disciplines Flexibility in handling new job assignments and achieving results in a rapidly changing work environment
Critical leadership capabilities:
- Ability to influence the way scientists think about problem solving Ability to integrate into a highly diverse team comprising multiple disciplines, nationalities, and cultural backgrounds Innovative, communicative, and result-oriented
Critical Success Factors & Key Challenges:
- Establish effective partnerships with laboratory, greenhouse, field and computational biologists Work productively in a team matrix environment with geographically dispersed stakeholders Identify and incorporate statistical methods from diverse scientific fields for application in biotechnology research Drive best practices for experimental design and process optimization to advance research goals
- All applicants must be eligible to work in the US.
- Travel Domestic and occasional international travel is required to field sites (occasionally for longer than 7 days), project meetings, and discussions with collaborators as well as to attend scientific conferences project meetings, and discussions with collaborators as well as to attend scientific conferences
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