Modeling Scientist

📋 Description

• Generate and apply model traceability for ecosystem models.
• Design and implement uncertainty quantification (parameter, structural, aleatory, epistemic).
• Apply sensitivity analysis, cross-validation, and multivariate testing for robustness.
• Quantify and communicate model confidence, bounds, and performance metrics.
• Develop hierarchical and Bayesian approaches for distributed model optimization.
• Integrate machine learning with process-based models to boost predictive performance.

🎯 Requirements

• 5+ years in uncertainty quantification and probabilistic modeling.
• Advanced Python and scientific computing proficiency; reproducible modeling pipelines.
• Strong software engineering practices: modular, testable, well-documented code.
• Deep commitment to scientific rigor, transparency, and integrity.
• Experience integrating ML with process-based or mechanistic models (preferred).
• Familiarity with DayCent or CESM; AWS and relational/spatial databases.
• Master’s or PhD in Statistics, Applied Mathematics, Environmental Science, or related field.