Modeling Scientist

• →Generate and apply a model traceability framework for ecosystem and biogeochemical models to enable rigorous model testing and improvements.
• →Design and implement an uncertainty quantification framework, including parameter, structural, aleatory, and epistemic uncertainties.
• →Apply sensitivity analysis, multivariate testing, and cross-validation to evaluate model robustness and generalizability.
• →Quantify and communicate model confidence, uncertainty bounds, and performance metrics.
• →Develop hierarchical and Bayesian approaches for distributed and iterative model optimization.
• →Apply probabilistic methods to integrate data, models, and uncertainty across scenarios.
• →Analyze model outputs to diagnose limitations and inform model improvement strategies.
• →Integrate machine learning techniques with process-based models to improve predictive performance.
• →Partner with data engineers to implement reproducible, scalable modeling pipelines.
• →Contribute to the design of model evaluation and optimization workflows.
• →Communicate uncertainty, confidence intervals, and model performance clearly to stakeholders.
• →Contribute to scientific reports, model documentation, and peer-reviewed publications.
• →Support defensible, auditable model outputs for regulatory and credit market review.

• Generate and apply model traceability framework for ecosystem and biogeochemical models to enable rigorous model testing and improvements
• Design and implement uncertainty quantification framework for the models, including parameter, structural, aleatory, and epistemic uncertainties
• Apply sensitivity analysis, multivariate testing, and cross-validation to evaluate model robustness and generalizability across space and time
• Quantify and communicate model confidence, uncertainty bounds, and performance metrics

• Develop hierarchical and Bayesian approaches to support distributed and iterative model optimization
• Apply probabilistic methods to integrate data, models, and uncertainty across scenarios
• Analyze model outputs to diagnose limitations and inform model improvement strategies

• Integrate machine learning techniques with process-based or mechanistic models to improve predictive performance and scalability
• Partner with data engineers to implement reproducible, scalable modeling pipelines
• Contribute to the design of model evaluation and optimization workflows

• Communicate uncertainty, confidence intervals, and model performance clearly to internal teams and external stakeholders
• Contribute to scientific reports, transparent model documentation, and peer-reviewed publications as appropriate
• Support defensible, auditable model outputs suitable for regulatory and credit market review

• 5+ years demonstrated experience in uncertainty quantification, probabilistic modeling, and data model integration
• Advanced proficiency in Python and scientific computing, with experience building reproducible modeling pipelines
• Strong software engineering practices, including writing modular, testable, and well-documented code
• Deep commitment to scientific rigor, transparency, and integrity
• Experience integrating machine learning with process-based or mechanistic models preferred
• Familiarity with ecosystem or Earth system models such as DayCent or CESM preferred
• Familiarity with cloud platforms and data systems, including AWS and relational or spatial databases, preferred
• Master’s or PhD degree or equivalent experience in Statistics, Applied Mathematics, Environmental Science, Earth System Science, Biology, or a related quantitative field

Only applicants currently, and in the future, eligible to work in the United States will be considered for this position. 

Summary: The Modeling Scientist is responsible for enhancing model traceability, uncertainty quantification, and predictive trustworthiness within Arva's ecosystem model predictions. This role is pivotal in advancing Arva’s platform for monitoring, reporting, and verifying greenhouse gas emission reductions and removals. Collaborating at the intersection of statistics, machine learning, and process-based ecosystem modeling, the Modeling Scientist ensures robust model traceability and uncertainty frameworks, delivering transparent, decision-ready outcomes for customers, partners, and environmental markets.