Quantitative Genetics Scientist Des Moines, Iowa, Research & Development Posted

Position: Quantitative Genetics Scientist Des Moines, Iowa, United States Research & Development Posted 2[...]
## Quantitative Genetics Scientist Des Moines, Iowa, United States

Job Location Des Moines, Iowa, United States
** Corteva Agriscience
** is a global leader in seed, crop protection, and digital solutions in agriculture. We have an opening in R&D for a research scientist to work alongside world-class researchers from statistics, genetics, computer science, and breeding to deliver superior products to our customers by accelerating genetic gain.

This position will reside within the Quantitative Genetics and Breeding Systems team of Breeding Technologies. This team discovers, develops, and deploys state-of-the-art genetic-statistical methods, computational algorithms, and software for the analysis of big data in high-throughput expert systems, experimental design studies, and breeding pipeline simulations. These systems enhance the knowledge and understanding of our germplasm and the genetic architecture of economically important traits to drive informed decisions in Corteva’s Seed Product Development.

We are looking for an inquisitive, detail-oriented, eager to learn and contribute quantitative geneticist, genetic statistician, biostatistician, animal breeder, or plant breeder with an interest in plant science and seed product design who wants to join a world-class Quantitative Genetics and Breeding Systems team.
** What You'll Do:
** 1. Develop, code into high-performance software, and deploy state-of-the-art quantitative-genetic models and methods for the analysis of phenotypic and genomic data to improve genetic gain in Corteva’s breeding programs, including to
* Recommend statistical analyses and processes for genomic selection using data from many field locations, accounting for spatial field trends, and exploiting genetic correlations between quantitative traits, locations, years, and different experiments.
* Develop and implement algorithms and methodology for the seamless incorporation of novel phenotypes from spatial, real-time, and longitudinal measurements, as well as novel genomic data into machine learning-based expert systems.
2. Develop, code into high-performance software, and deploy deterministic and stochastic simulations for the analysis of breeding pipelines across crops and regions. This includes:
* Connect software to in-house databases.
* Develop memory and runtime-efficient software.
* Develop prototypic user interfaces to allow our breeding community to access the tools and communicate results.
3. Collaborate with software architects and engineers to implement high-throughput analytic systems into internal analysis systems and pipelines.
4. Collaborate with plant breeders from multiple crops and geographies that span the globe to understand biological questions and develop genetic-statistical solutions to complex questions.
5. Provide consultation and training to Corteva’s worldwide research community regarding the implementation of experimental designs, breeding schemes, models, and methods.
6. Provide expertise to external collaborations.
7. Develop R Shiny applications for customized data analyses, visualizations of data and results.
8. Stay current with the scientific progress in the literature and recognize the potential of novel methods for Corteva’s seed product development.
9. Publishing scientific articles is encouraged.
** What Skills You Need:
*** Ph.D. in Quantitative Genetics, Animal Breeding, Plant Breeding, Biostatistics, or a related field.
* A solid foundation in quantitative-genetic and statistical theory and an interest in applied statistical analyses and breeding systems optimizations.
* Strong oral and written communication skills, including the ability to communicate effectively with individuals of diverse language, cultural, and scientific backgrounds.
* Solid foundation in quantitative genetics, animal or plant breeding, and a basic understanding of a commercial breeding and/or seed business.
* Solid foundation in statistical analysis methods, including mixed linear models, Bayesian models, dimension reduction techniques, and machine learning methods.
* Has demonstrated statistical computing skills
* Knowledge of R, Python, or related programming tools.
*…