Geospatial Scientist– Research and Development

Listing Info AgReliant Genetics is the fastest growing independent seed company in the industry and currently the 3rd largest corn seed company in the United States. Our goal is to offer the best seed products to our North American customers through superior research, breeding and production techniques by focusing ONLY on seed. Headquartered in Westfield, Indiana, AgReliant Genetics is owned by two of the largest independent seed companies in the world. KWS and Limagrain bring over 200 years of combined seed experience to AgReliant Genetics. Innovation, superior service and exceptional customer value are the center point of our multiple brand offering. AgReliant Genetics offers the best seed products available through the brands; AgriGold, LG Seeds, Great Lakes Hybrids, Wensman, Producers Hybrids, Eureka, and Pride. The cornerstone of the success of AgReliant Genetics is our people. At every level, our employees have a hand in maintaining the company’s reputation and furthering our growth. That’s why we pursue excellent people and do our best to provide a challenging and rewarding work experience. RESPONSIBILITIES: The successful candidate will contribute to research and development for high-throughput phenotyping and precision agriculture within the frame of an applied corn breeding program. The candidate will directly report to the Biostatistics Manager. Occasional travel is required. The Research Scientist Will o Scout for relevant technologies to evaluate and implement novel precision phenotyping/geospatial analytics strategies for corn o Interact with public and private service providers or collaborators o Design field experiments in collaboration with the corn breeders o Visit field plots, participate in field tours and trainings as well as collect and store data o Stay abreast of relevant  mathematical and statistical approaches to evaluate and implement novel phenotypic data analysis methodologies which accommodate increased data quantity and improved data quality o Design and test algorithms and conduct prototyping to evaluate possible scenarios leveraging computational and statistical techniques for the development of novel approaches for high-throughput data analyses o Collaborate with the breeding community to integrate the data obtained from field experimentations into corn breeding schemes for genetic improvement of the germplasm o Communicate clearly the interpretation of the experimental results using both written and verbal skills with colleagues and collaborators o Maintain the highest level of confidentiality, customer service and professionalism o Promote a positive image of the company and the research department o Support management decisions regarding strategy of the research department o Perform other duties as required Duties and responsibilities may be revised as needed by the Biostatistics Manager and/or Director of Plant Breeding Technology either verbally and/or in writing. Qualifications o PhD or equivalent experiences in biostatistics, statistics, mathematics, spatial statistics, geoinformatics, environmental modeling, engineering, computer science, computational biology or related discipline o Solid bases in statistics with knowledge in statistical analyses for large and complex multivariate trait datasets o Proficiency in image analysis and Spatial and/or Temporal Statistical Modeling o Experience with the design, analysis, and interpretation of field data o Experience with procedural programming in standard general-purpose statistical software  to develop scripts for phenotypic data analysis o Excellent oral and written communication skills and ability to collaborate with laboratory scientists and breeders Desired skills/experience o 3+ yrs. experience post PhD building Spatio-Temporal models and predictive models is highly desired o Proficiency in image analysis, spatial auto-correlation modeling, building spatial econometric models, or Topological data analysis o Pattern Recognition (Conditional Random Fields, Hidden Markov Models, Maximum Entropy Markov Models, etc) o Proficiency in Machine learning algorithms and concepts (Ensembles, Deep Learning, SVM, etc.) o Experience in stochastic modeling and simulation o Experience working with agricultural/biological scientific data is highly desired