Perennial is building the world’s leading verification platform for soil-based carbon removal. Our vision is to unlock soil as one of the world’s largest carbon sinks. To do that, we are building trusted standards, tools, and technologies to help verify climate-smart agriculture.
Requirements
- Strong proficiency in Python for data science (e.g. pandas, scikit-learn, xarray, numpy)
- Experience building machine learning, statistical, or time series models informed by remotely-sensed data or large spatial datasets
- Experience working in the soil carbon MRV space and familiarity with relevant methodologies and tools (e.g. VM0042, VM0032, VMD0053)
- Expertise in the open source geospatial python stack. Basic raster and vector operations, e.g. resampling, tiling, clipping, extracting, spatial statistics, harmonizing data
- Experience quantifying uncertainty of spatial maps, or more generally geostatistics or spatial stats, esp. with machine learning
- Experience working with Google Earth Engine and GCS
Responsibilities
- Build, improve, and deploy machine learning models for predicting soil carbon stock with remotely-sensed covariate data and limited training data
- End-to-end deliveries for our customers: train models, run predictions, and ensure quality results are delivered in customer reports
- Work with other data scientists, engineers, and policy experts to ensure that our data and methods comply with various standards and methodology requirements specific to a given project
- Characterize the accuracy and uncertainty of model predictions and demonstrate the dependence of performance metrics on the surrounding context and parameters of carbon projects
- Execute efficiently throughout full development cycle, from performing exploratory data analysis and initial R&D to rapid prototyping and hardening models for production
- Communicate your research internally and externally through detailed documentation, conference presentations, and peer-reviewed publications
Other
- Master's degree or Ph.D. in statistics, math, computer science, remote sensing, AI/ML, ecosystem science, geography, or a related STEM field
- 3–6 years of industry or research experience in data science, applied ML, geospatial analysis, or related fields
- Good communication and collaboration skills with functional and cross-functional teams
- Ability to independently manage a project and deliver results
- Startup experience or a strong entrepreneurial mindset (generally private company experience)
