Machine Learning Scientist

Job Title: Machine Learning Scientist (Uncertainty Quantification)

Department: Science / Modeling & Analytics

Reports to: Lead Modeling Scientist

Location: Berkeley CA (Hybrid schedule)

Level: 4 (Experience Contributor)

Base Salary Range: $100k - $130k base salary

General Position Description

The Modeling Scientist Uncertainty Quantification is responsible for leading the development and application of statistical probabilistic and machine learning approaches that quantify confidence in Arvas ecosystem model predictions. This role is central to advancing Arvas 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 Responsibilities

Uncertainty Quantification and Model Evaluation

• Design and implement uncertainty quantification frameworks for ecosystem and biogeochemical models including parameter input and structural uncertainty
• 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 calibration 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 as appropriate
• Support defensible auditable model outputs suitable for regulatory and credit market review

Key Competencies / Requirements

• 5 years demonstrated experience in uncertainty quantification probabilistic modeling and data model integration
• MUST HAVE: 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
• Masters or PhD degree or equivalent experience in Statistics Applied Mathematics Environmental Science Earth System Science Biology or a related quantitative field

Employment Eligibility

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

About Arva Intelligence

Arva is a machine learning software-based SaaS company with offices located in Houston TX and Park City UT. Arvas 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.