Job description
Role Overview
Department: Modeling & Analytics
Reports to: Lead Modeling Scientist
Location: Remote
Base Salary Range: $100k - $160k base salary
This role is responsible for improving model traceability, uncertainty quantification, and predictive trustworthiness in Arva's ecosystem model predictions. You will work at the intersection of statistics, machine learning, and process-based ecosystem modeling to advance our monitoring, reporting, and verification platform for greenhouse gas emission reductions and removals.
Primary Responsibilities
Uncertainty Quantification and Model Evaluation
- 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 across space and time.
- Quantify and communicate model confidence, uncertainty bounds, and performance metrics.
Statistical and Probabilistic Modeling
- 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.
Machine Learning and Model Integration
- 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.
Scientific Communication and Documentation
- 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.
- Support defensible, auditable model outputs suitable for regulatory and credit market review.
Key Competencies and Requirements
- 5 years of 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.
- Master's or PhD degree or equivalent experience in Statistics, Applied Mathematics, Environmental Science, Earth System Science, Biology, or a related quantitative field.
Preferred Qualifications
- Experience integrating machine learning with process-based or mechanistic models.
- Familiarity with ecosystem or Earth system models such as DayCent or CESM.
- Familiarity with cloud platforms and data systems, including AWS and relational or spatial databases.
Employment Eligibility
Only applicants currently, and in the future, eligible to work in the United States will be considered for this position.