Arva Intelligence

Role Description 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. Qualifications - 5+ years demonstrated experience in uncertainty quantification, probabilistic modeling, and data model integration - Master’s or PhD degree or equivalent experience in Statistics, Applied Mathematics, Environmental Science, Earth System Science, Biology, or a related quantitative field - 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 Requirements - 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. Benefits - $100k - $160k base salary Company Description Only applicants currently, and in the future, eligible to work in the United States will be considered for this position.

Role Description The Data Engineer is responsible for building and scaling the data and computational backbone that supports Arva’s ecosystem modeling and measurement, reporting, and verification platforms. This role sits within a multidisciplinary Data Science team and focuses on designing reliable, auditable, and scalable data systems that enable biogeochemical modeling and optimization at production scale. In this role, the Data Engineer will design and maintain production-grade data pipelines that integrate diverse datasets including field measurements, management practices, soils, and weather with process-based ecosystem models. The role plays a critical part in ensuring data quality, reproducibility, and traceability so that scientific outputs can be translated into trusted, credit-grade results with real-world impact. Qualifications - 3+ years demonstrated experience building and maintaining data pipelines for large, complex, and heterogeneous datasets - Strong proficiency in Python and modern data engineering tools, with experience writing production-grade, testable code - Experience working with cloud platforms, with AWS strongly preferred - Familiarity with containerization tools such as Docker and version control systems such as GitHub - Experience with relational and spatial databases, including PostgreSQL and PostGIS - Experience working with geospatial data formats and spatial data processing - Experience supporting scientific or ecosystem modeling workflows preferred - Familiarity with workflow orchestration tools such as Airflow or Prefect preferred - Bachelor’s or Master’s degree or equivalent experience in Data Engineering, Computer Science, Environmental Informatics, or a related field Requirements - Design, implement, and maintain scalable data pipelines supporting ecosystem and biogeochemical modeling - Build reproducible workflows that generate standardized model inputs and manage outputs across space, time, and scenario analysis - Integrate heterogeneous datasets, including field data, management data, soil data, and weather data, into modeling pipelines - Develop and maintain cloud-based infrastructure to support modeling pipelines and optimization workflows - Implement data storage solutions using relational, spatial, and object-based databases - Support efficient data access and processing using platforms such as PostgreSQL, PostGIS, and cloud object storage - Ensure data quality, versioning, traceability, and auditability to support measurement, reporting, and verification requirements - Implement validation and monitoring processes to ensure reliability of model inputs and outputs - Support transparent, repeatable workflows suitable for regulatory and credit market review - Write clean, modular, and well-documented production code that supports maintainable and scalable data systems - Apply software engineering best practices including testing, version control, and documentation - Collaborate closely with Data Science and Technology teams to align data infrastructure with modeling, analytics, and production needs Benefits - $95k - $130k base salary range