{"schemaVersion":"jobsearcher.job.v1","id":"fcef8046b79bbf88e13aa924","url":"https://jobsearcher.com/jobs/fcef8046b79bbf88e13aa924","canonicalUrl":"https://jobsearcher.com/jobs/fcef8046b79bbf88e13aa924","title":"Machine Learning Scientist","description":"Job Title: Machine Learning Scientist (Uncertainty Quantification)Department: Science / Modeling & AnalyticsReports to: Lead Modeling ScientistLocation: Berkeley, CA (Hybrid schedule)Level: 4 (Experience Contributor)Base Salary Range: $100k - $130k base salaryGeneral Position DescriptionThe Modeling Scientist, Uncertainty Quantification is responsible for leading the development and application of statistical, probabilistic, and machine learning approaches that quantify confidence 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 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.Primary Job ResponsibilitiesUncertainty Quantification and Model EvaluationDesign and implement uncertainty quantification frameworks for ecosystem and biogeochemical models, including parameter, input, and structural uncertaintyApply sensitivity analysis, multivariate testing, and cross-validation to evaluate model robustness and generalizability across space and timeQuantify and communicate model confidence, uncertainty bounds, and performance metricsStatistical and Probabilistic ModelingDevelop hierarchical and Bayesian calibration approaches to support distributed and iterative model optimizationApply probabilistic methods to integrate data, models, and uncertainty across scenariosAnalyze model outputs to diagnose limitations and inform model improvement strategiesMachine Learning and Model IntegrationIntegrate machine learning techniques with process-based or mechanistic models to improve predictive performance and scalabilityPartner with data engineers to implement reproducible, scalable modeling pipelinesContribute to the design of model evaluation and optimization workflowsScientific Communication and DocumentationCommunicate uncertainty, confidence intervals, and model performance clearly to internal teams and external stakeholdersContribute to scientific reports, transparent model documentation, and peer-reviewed publications as appropriateSupport defensible, auditable model outputs suitable for regulatory and credit market reviewKey Competencies / Requirements5+ years demonstrated experience in uncertainty quantification, probabilistic modeling, and data model integrationMUST HAVE: Advanced proficiency in Python and scientific computing, with experience building reproducible modeling pipelinesStrong software engineering practices, including writing modular, testable, and well-documented codeDeep commitment to scientific rigor, transparency, and integrityExperience integrating machine learning with process-based or mechanistic models preferredFamiliarity with ecosystem or Earth system models such as DayCent or CESM preferredFamiliarity with cloud platforms and data systems, including AWS and relational or spatial databases, preferredMaster’s or PhD degree or equivalent experience in Statistics, Applied Mathematics, Environmental Science, Earth System Science, Biology, or a related quantitative fieldEmployment EligibilityOnly applicants currently, and in the future, eligible to work in the United States will be considered for this position.About Arva IntelligenceArva is a machine learning software-based SaaS company with offices located in Houston, TX and Park City, UT. Arva's platform was built to apply our novel ML technology to the agricultural industry, optimizing and measuring regenerative practices, improving crop yields, and reducing operational costs for producers. Our platform helps our customers and partners capitalize on \"natural regenerative practices\" by providing recommendations that improve environmental and ecological ecosystems. Platform features include practice verification and registration, as well as the sale of environmental asset credits to our corporate buyers. Thus, Arva is helping to keep the planet green by providing a \"green-tech\" platform that informs, measures, validates, predicts, and registers carbon exchange opportunities, allowing growers and ranchers to produce and sell credits that are bought by our corporate partners, who endorse sustainable food supply and carbon neutrality.This job description reflects the core duties of the role but is not intended to be all-inclusive. The role may evolve as the company grows, requiring additional responsibilities or changes in scope.","company":"Arva","rawCompany":"arva","city":"Berkeley","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-04-09T08:01:51.039Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"19-2099.01","title":"Remote Sensing Scientists and Technologists","slug":"remote-sensing-scientists-and-technologists"}],"industries":[{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"},{"code":"541620","title":"Environmental Consulting Services","slug":"environmental-consulting-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Scientist","description":"Job Title: Machine Learning Scientist (Uncertainty Quantification)Department: Science / Modeling & AnalyticsReports to: Lead Modeling ScientistLocation: Berkeley, CA (Hybrid schedule)Level: 4 (Experience Contributor)Base Salary Range: $100k - $130k base salaryGeneral Position DescriptionThe Modeling Scientist, Uncertainty Quantification is responsible for leading the development and application of statistical, probabilistic, and machine learning approaches that quantify confidence 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 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.Primary Job ResponsibilitiesUncertainty Quantification and Model EvaluationDesign and implement uncertainty quantification frameworks for ecosystem and biogeochemical models, including parameter, input, and structural uncertaintyApply sensitivity analysis, multivariate testing, and cross-validation to evaluate model robustness and generalizability across space and timeQuantify and communicate model confidence, uncertainty bounds, and performance metricsStatistical and Probabilistic ModelingDevelop hierarchical and Bayesian calibration approaches to support distributed and iterative model optimizationApply probabilistic methods to integrate data, models, and uncertainty across scenariosAnalyze model outputs to diagnose limitations and inform model improvement strategiesMachine Learning and Model IntegrationIntegrate machine learning techniques with process-based or mechanistic models to improve predictive performance and scalabilityPartner with data engineers to implement reproducible, scalable modeling pipelinesContribute to the design of model evaluation and optimization workflowsScientific Communication and DocumentationCommunicate uncertainty, confidence intervals, and model performance clearly to internal teams and external stakeholdersContribute to scientific reports, transparent model documentation, and peer-reviewed publications as appropriateSupport defensible, auditable model outputs suitable for regulatory and credit market reviewKey Competencies / Requirements5+ years demonstrated experience in uncertainty quantification, probabilistic modeling, and data model integrationMUST HAVE: Advanced proficiency in Python and scientific computing, with experience building reproducible modeling pipelinesStrong software engineering practices, including writing modular, testable, and well-documented codeDeep commitment to scientific rigor, transparency, and integrityExperience integrating machine learning with process-based or mechanistic models preferredFamiliarity with ecosystem or Earth system models such as DayCent or CESM preferredFamiliarity with cloud platforms and data systems, including AWS and relational or spatial databases, preferredMaster’s or PhD degree or equivalent experience in Statistics, Applied Mathematics, Environmental Science, Earth System Science, Biology, or a related quantitative fieldEmployment EligibilityOnly applicants currently, and in the future, eligible to work in the United States will be considered for this position.About Arva IntelligenceArva is a machine learning software-based SaaS company with offices located in Houston, TX and Park City, UT. Arva's platform was built to apply our novel ML technology to the agricultural industry, optimizing and measuring regenerative practices, improving crop yields, and reducing operational costs for producers. Our platform helps our customers and partners capitalize on \"natural regenerative practices\" by providing recommendations that improve environmental and ecological ecosystems. Platform features include practice verification and registration, as well as the sale of environmental asset credits to our corporate buyers. Thus, Arva is helping to keep the planet green by providing a \"green-tech\" platform that informs, measures, validates, predicts, and registers carbon exchange opportunities, allowing growers and ranchers to produce and sell credits that are bought by our corporate partners, who endorse sustainable food supply and carbon neutrality.This job description reflects the core duties of the role but is not intended to be all-inclusive. 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