Research Scientist Agricultural Statistics & Modeling

Role Overview: You will be responsible for designing and implementing statistical methodologies for experiment design, model calibration, and validation of process-based models. Your role will involve applying frequentist and Bayesian approaches to uncertainty quantification and predictive modeling. Additionally, you will work with process-based models such as DayCent and DNDC to ensure robust statistical validation of outputs. Conducting spatial modeling and geostatistical analyses for large-scale agricultural datasets will also be part of your responsibilities. You will develop and implement statistical techniques to support Monitoring, Reporting, and Verification (MRV) frameworks for carbon projects. Automation and optimization of statistical workflows using Python will be a key aspect of your role. Collaborating with interdisciplinary teams, including environmental modelers, agronomists, and data scientists, to integrate statistical insights into decision-making processes will also be essential. Communication of findings through high-quality reports, technical documentation, and peer-reviewed publications will be part of your regular tasks. Key Responsibilities: - Design and implement statistical methodologies for experiment design, model calibration, and validation of process-based models - Apply frequentist and Bayesian approaches to uncertainty quantification and predictive modeling - Work with process-based models such as DayCent and DNDC for robust statistical validation of outputs - Conduct spatial modeling and geostatistical analyses for large-scale agricultural datasets - Develop and implement statistical techniques to support Monitoring, Reporting, and Verification (MRV) frameworks for carbon projects - Ensure compliance with carbon protocols (VM0042, VMD0053) and support statistical reporting for project validation - Automate and optimize statistical workflows using Python - Collaborate with interdisciplinary teams to integrate statistical insights into decision-making processes - Communicate findings through high-quality reports, technical documentation, and peer-reviewed publications Qualifications: - Masters or Ph.D. in Statistics, Data Science, Environmental Science, Agronomy, or a related field - Strong expertise in frequentist and/or Bayesian statistics, experimental design, and model validation - Proficiency in Python for statistical computing, data analysis, and visualization - Strong analytical, problem-solving, and communication skills - Proven ability to produce high-quality reports and scientific publications Preferred Qualifications: - Experience in spatial modeling and geostatistics - Understanding of agriculture, soil science, and ecosystem dynamics - Hands-on experience with process-based models (DayCent, DNDC, or similar) - Familiarity with carbon credit methodologies (VM0042, VMD0053) (Note: Additional Information section has been omitted as it did not contain relevant details for the job description),