Remote Agronomic Data Scientist

Digital Disease and Insect ManagementCandidate will be involved in many digital disease and insect management projects within our farming solutions and digital department. The role will consist of data collection, data scrubbing, building models using machine learning approaches to solve complex agronomic problems. These agronomic projects will focus on combining agronomic data, weather data, soil data and imagery to explain biological questions related to abiotic and biotic stresses on plants. These digital projects will focus in supporting our crop protection products to provide better efficacy and timing to improve return of investment to our customers.Required SkillsSolid foundation in probability and statistical inferenceExtensive experience analyzing designed experiments and observational datasets (e.g. linear fixed effects and mixed effects models, regression analysis)General Data Engineering knowledgePython (GIS in Python)Experience with variable selection, dimensionality reduction, model diagnostics, remedial measures and validation.Experience consuming APIs (experience building APIs would be a plus)Written and verbal communication skills and the ability to successfully collaborate and lead projects with colleagues from diverse technical backgroundCritical thinking, problem-solving skills, flexibility, willingness to learnPreferred SkillsExperience with generalized linear models and non-linear modelsExperience with ML package; XG BoostExperience consulting on scientific projects or working within a scientific teamExperience with machine learning/AI/deep learning (both tabular and neuro networks)Preferred: M.S. graduate in data science with biology concentration or agronomy; graduates with statistics and computer science degrees will also be considered. Candidate should have strong skillsets in statistics, mathematics, data science, biology and python programming.Preferred Experience: Involves data science or biology publications, some industry or intern/co-op experience or having work projects/capstones involving data science and ML to solve complex biological problems