Monsanto is seeking a highly motivated individual to become an integral member of a cross functional crop biometrics organization. The Statistical Geneticist will be involved in large scale analyses of plant breeding data to support Monsanto's global breeding effort. The successful candidate will develop generalized procedures to evaluate and improve the effectiveness of Monsanto's selection and testing programs at different stages of product development and optimize product placement strategies.
The Statistical Geneticist will have a strategic role in determining how we leverage molecular data to better drive our field breeding and technology development efforts. They will have an opportunity to have a direct impact on our global breeding activities by evaluating overall effectiveness of the programs and optimizing strategies across all cropping platforms.
- Develop and test enhanced methods of predicting the performance of populations, and lines
- Provide solid interpretation and recommendations based on quantitative data analysis to drive improvements to the existing breeding practices.
- Provide written and oral presentations of methods, results, conclusions, and recommendations to peer and management groups.
- PhD in Plant Breeding, Genetics, Statistics, or related field of study.
- Expertise in the application of quantitative genetics methodology.
- Proficiency with commercial statistical software (e.g., SAS, R, ASREML, etc.) or custom (Fortran, C++, C#, etc.) programs for the design and analysis of experiments.
- Experience working with large databases.
- Significant experience in marker-assisted selection, molecular breeding or genomic selection.
- Strong understanding of plant breeding.
- A solid foundation in statistical theory and with a strong interest in applied statistical analysis methodologies.
- Direct experience in agronomy and/or plant breeding in vegetables or row crops.
- Excellent communication skills, with the ability to summarize complex concepts in language understandable by scientists from a variety of disciplines.
- Previous experience with mixed-model methods for genetic evaluation is a plus.
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