What You'll Do
Analyze experimental and long-term observational data to understand forest productivity and ecosystem dynamics. You’ll assess and refine field trial designs using power analyses and advanced statistical techniques, ensuring reliable results across multi-year studies.
Develop and implement models that link tree growth and health to soil microbiomes and environmental variables, incorporating spatial and temporal patterns. Help modernize carbon accounting by integrating genomic, remote sensing, and field data into scalable systems.
Build and validate pipelines to evaluate satellite and drone data against ground truth measurements, improving monitoring accuracy. Collaborate with science and growth teams to deliver insights that accelerate research and deployment. Occasional travel (10–15%) supports fieldwork, team gatherings, and partner engagement.
Requirements
- Master’s or doctoral degree in Statistics, Applied Mathematics, or a related quantitative discipline
- Proven experience with mixed-effects and hierarchical models, longitudinal data, repeated measures, and spatio-temporal modeling
- Strong programming skills in SQL, R, and Python for data processing, analysis, and modeling
- Applied experience using machine learning in biological or environmental systems
- Experience with data integration, version control (Git), and reproducible research tools like Jupyter, Quarto, or Markdown
- Ability to balance methodological rigor with practical impact—favoring interpretable models when appropriate
- Deep interest in climate resilience, biodiversity, and ecosystem restoration
Preferred Qualifications
- Background in forest ecology, soil microbial communities, or field-based experimental design
- Experience analyzing geospatial or remote sensing datasets
Benefits
- Equity in a fast-growing public benefit company
- Flexible time off policy
- Medical, dental, and vision coverage
- Wellness reimbursement program
- Relocation support available for candidates moving to Austin
