• Formulates and leads/co-leads novel projects with team or enables matrix collaboration on project/technology solutions to achieve creative results for impact on BR goals • Generates innovative ideas within own team and/or project team/functional community to meet new technical requirements and/or answer project key scientific/technical/development questions • Establishes target dates and priorities to enable data-driven advancements in project teams, within own team, and with collaborators, or within functional community • Oversees the progress of the study and for ensuring that the study is conducted, recorded and reported according to the study protocol • Ensures that the study is compliant with the appropriate GLP regulations, Novartis animal welfare policies, CRO in-house standard operating procedures, Novartis expert recommendations (where feasible) and all relevant international regulatory guidelines/regulations

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.

• Manage day-to-day project operations in roles such as principal investigator, project director, or task lead, ensuring deliverables are completed on time, within budget, and to a high standard. • Lead collaborative project teams by developing timelines, coordinating staff assignments. • Identify research problems and design rigorous applied studies, including aligned research questions, methodologies, and analytic plans. • Apply subject-matter expertise to design and implement relevant, responsive, and impactful research and technical assistance projects. • Develop study designs with aligned research questions, methodologies, and analytic plans. • Lead and contribute to proposals for federal, state, foundation, and other clients. • Devise and implement innovative solutions to practical challenges in applied research. • Lead data collection and analysis efforts, including the design of qualitative and/or quantitative data collection tools (e.g., interview protocols, focus group guides, surveys). • Supervise field-based data collection activities, including interviews, observations, focus groups, and document reviews. • Conduct or oversee data analysis using appropriate tools and techniques for various data types. • Communicate study progress and findings clearly and effectively through reports, briefs, visualizations, and other materials. • Represent the organization professionally in client interactions and manage client and stakeholder relationships with awareness of the broader policy and practice landscape. • Communicate effectively, both orally and in writing, with clients, partners, and stakeholders, and foster positive, collaborative working relationships. • Contribute thoughtfully and creatively to project teams.

Provide research and administrative support for global health projects, manage data workflows, assist with regulatory compliance, and contribute to research proposal development and faculty programming within the Division of Global Health Equity.