Computational Breeding Analyst

Who are we, and what do we do?
At Corteva Agriscience, you will help us grow what’s next. No matter your role, you will be part of a team that is building the future of agriculture – leading breakthroughs in the innovation and application of science and technology that will better the lives of people all over the world and fuel the progress of humankind.

The Computational Breeding Analyst will be accountable for the pipeline development, implementation, execution and interpretation of analyses necessary to make strategic decisions to develop lines, varieties, and hybrids across the Seed Product Development (SPD) for Latin America and Africa (LAAF).

Responsibilities
How will you help us grow? It matters to us, and it matters to you
This position will drive several key analyses optimizing or improving applied breeding programs. Examples of the types of work expected would include:

1. Automate analytical pipelines in R; Assist the implementation of new approaches; Clearly communicate and educate analytical changes to breeders, leadership, and broader audience.
2. Streamline the optimization breeding program models to improve breeding methods, breeding operations, data collection, and logistics; Quantify marginal improvements of genetic gains and accuracy per unit of cost/investment. Drive data-informed strategies for selection of populations and progenies.
3. Develop pipelines and methodologies to characterize target population of environments and trial distribution using GxE patterns and environmental classification; improving estimation datasets optimized for key testing patterns.

Requirements:
What expertise have you grown? What do you bring to the table?

1. PhD in Plant or Animal Breeding, Biometrics or Quantitative Genetics, with an emphasis in the areas of statistical genetics and data analytics.
2. Comprehensive knowledge and applied research experience in plant breeding, knowledge of statistics, biometrics, quantitative genetics, genome wide prediction methods, and phenotypic data analyses. Familiarity with modeling through mixed models, Bayesian methods, and machine learning. Programming skills to script in R and key packages (Rcpp, Shiny, Leaflet, and Tidyverse).
3. Strong verbal and written communication skills and a proven ability to work well in a team environment. Ability to communicate complex, quantitative results in a concise/simple format. Excellent personal interaction skills required for effectively developing strong internal and external working relationships with a diverse group of people.
4. Fluency in English is fundamental, Portuguese and or Spanish would be a bonus.
5. An independent and self-motivated scientist is required.
6. Preferred if the candidate is based in Latin America or Africa.