Modeling Scientist

Arva Intelligence•Houston, TX

2d•$100,000 - $160,000•Remote

About The Position

The Modeling Scientist is responsible for improving model traceability, uncertainty quantification, and predictive trustworthiness in Arva’s ecosystem model predictions. This role is central to advancing Arva’s monitoring, reporting, and verification platform for greenhouse gas emission reductions and removals. Working at the intersection of statistics, machine learning, and process-based ecosystem modeling, this role works closely with ecosystem modelers and data engineers to design robust model traceability and uncertainty frameworks that support transparent, decision-ready outputs for customers, partners, and environmental markets. The Modeling Scientist plays a critical role in translating scientific rigor into real-world impact through credible, auditable modeling systems.

Requirements

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
Master’s or PhD degree or equivalent experience in Statistics, Applied Mathematics, Environmental Science, Earth System Science, Biology, or a related quantitative field

Nice To Haves

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

Responsibilities

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.