Job
Description
Job Description: You will be responsible for designing and implementing statistical methodologies for experiment design, model calibration, and validation of process-based models. You will apply frequentist and Bayesian approaches to uncertainty quantification and predictive modeling. Working with process-based models such as DayCent and DNDC, you will ensure robust statistical validation of outputs. Additionally, you will conduct spatial modeling and geostatistical analyses for large-scale agricultural datasets. Your role will involve developing and implementing statistical techniques to support Monitoring, Reporting, and Verification (MRV) frameworks for carbon projects. You will also ensure compliance with carbon protocols (VM0042, VMD0053) and support statistical reporting for project validation. Automation and optimization of statistical workflows using Python will be a key aspect of your responsibilities. Collaboration with interdisciplinary teams, including environmental modelers, agronomists, and data scientists, to integrate statistical insights into decision-making processes will also be a part of your role. Lastly, you will 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 about the company was not provided in the job description. Job Description: You will be responsible for designing and implementing statistical methodologies for experiment design, model calibration, and validation of process-based models. You will apply frequentist and Bayesian approaches to uncertainty quantification and predictive modeling. Working with process-based models such as DayCent and DNDC, you will ensure robust statistical validation of outputs. Additionally, you will conduct spatial modeling and geostatistical analyses for large-scale agricultural datasets. Your role will involve developing and implementing statistical techniques to support Monitoring, Reporting, and Verification (MRV) frameworks for carbon projects. You will also ensure compliance with carbon protocols (VM0042, VMD0053) and support statistical reporting for project validation. Automation and optimization of statistical workflows using Python will be a key aspect of your responsibilities. Collaboration with interdisciplinary teams, including environmental modelers, agronomists, and data scientists, to integrate statistical insights into decision-making processes will also be a part of your role. Lastly, you will 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 about the company was not provided in the job description.
