Key Skills

Job Description

Role & Team We are excited to add a Staff Data Scientist, Wildfire to our team. This individual will lead the scientific foundation of our Fuel Detection Model, the core engine that translates satellite and environmental data into an understanding of vegetation structure, fuel loads, and wildfire risk. Working alongside ML engineers, and a product team, you'll define accuracy for our models, design the research and validation methods that prove it, and ensure our modeling choices are grounded in fire science and remote sensing fundamentals. This is a great opportunity for someone who is energized by open scientific questions with direct real-world stakes, and who wants their research to shape how utilities prevent catastrophic wildfires. Time Zone Requirement: North America (NST, AST, EST, CST, MST, PST) What You’ll Do Lead research into how vegetation structure, fuel conditions, and wildfire risk can be estimated from satellite, LiDAR, and environmental data across diverse geographies Design rigorous validation and evaluation methodologies, including ground-truth strategies, uncertainty quantification, and error analysis tied to real-world impact Prototype and refine ML modeling approaches, then partner with ML engineers to translate them into production systems Integrate established fire science, such as fuel models and fire behavior frameworks, with data-driven methods Define scientific standards for experimentation, reproducibility, and model interpretability across the modeling organization Communicate research findings clearly to engineers, product teams, customers, and the broader wildfire science community Mentor ML engineers on scientific methodology and domain reasoning Skills & Experience 8+ years of applied research or data science experience in wildfire science, fire ecology, forestry, remote sensing, atmospheric science, or a related quantitative field Deep expertise in remote sensing and geospatial analysis, including working with satellite imagery and large-scale environmental datasets Strong statistical modeling and machine learning skills in Python, with tools like GeoPandas, scikit-learn, PyTorch, or XGBoost Track record of designing validation studies and evaluation frameworks for environmental or geospatial models Excellent communication skills, with the ability to make complex scientific work legible across technical and non-technical audience Nice To Have Familiarity with fire behavior or fuels modeling frameworks (e.g., Rothermel-based models, LANDFIRE fuel classifications) Experience integrating physics-based models with ML, or with active learning and uncertainty quantification Peer-reviewed publications in wildfire science, remote sensing, or environmental modeling Familiarity with GCP, Vertex AI, or similar cloud-based platforms Experience in remote-first or globally distributed team Note: We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description. What We Offer Competitive, location-specific compensation and benefits Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around Home office stipend, co-working and ongoing education budgets A company culture that genuinely embodies each of our core values To be part of truly mission-driven work that reduces wildfires, protects earth’s natural resources and helps solve our climate crisis

Core Responsibilities

Lead research and develop rigorous validation methods for estimating vegetation structure, fuel conditions, and wildfire risk from satellite, LiDAR, and environmental data. Establish scientific standards and integrate fire science with machine learning, partnering with engineers and product teams to deliver interpretable production models and communicate findings.

Requirements

Requires 8+ years of applied research or data science experience in wildfire science or a related quantitative field, with deep expertise in remote sensing, geospatial analysis, statistical modeling, and machine learning. Candidates should be proficient in Python and experienced in designing validation frameworks, analyzing environmental or geospatial models, and communicating complex research to varied audiences.

Benefits

• Competitive Location-Specific Compensation And Benefits
• Flexible And Autonomous Working Environment
• Home Office Stipend
• Co-Working Budget
• Ongoing Education Budget