NCEAS Wildfire Resilience Index Data Analyst; Associate Specialist National Center Ecological Analysis

Position: NCEAS Wildfire Resilience Index Data Analyst (Associate Specialist) in the National Center for Ecological Analysis and
The National Center for Ecological Analysis and Synthesis (NCEAS) at UCSB seeks a data analyst to support a research program focused on constructing a retrospective analysis of our a Wildfire Resilience Index (WRI) for the US and Canadian West (https://(Use the "Apply for this Job" box below).). Built on the success of the NCEAS' Ocean Health Index, the Wildfire Resilience Index will provide policymakers and the public with clear, actionable metrics on wildfire resilience to improve management.

In this project, we will be retrospectively calculating the WRI for the period . This retrospective effort will allow us both to assess the performance of the index over time and to evaluate the efficacy of wildfire treatments, management strategies, and interventions at spatial and temporal scales that have not previously been possible. As part of this multi- year project, you will work together as part of a dynamic project team, including an expert working group, a project lead, and a web developer.

Together, this team is advancing the first long-term, quantitative, applied assessment of wildfire resilience across the western United States and Canada. This position will be based at NCEAS in Santa Barbara, California.

Position Description

As a successful Data Analyst, you will bring strong analytical skills and domain knowledge to support the development of a suite of indicators and a comprehensive framework to quantitatively measure wildfire resilience at scales directly relevant to management. This position offers significant opportunities to apply and expand a diverse analytical skillset at the critical interface of ecological synthesis science and applied management solutions.

You will play a key role in building robust data pipelines and reproducible analytical workflows to integrate heterogeneous sources of data-including tabular datasets, geospatial information, and remote sensing products-into the project framework. A central part of this work will involve the use of advanced machine learning methods, including AI-driven approaches, to accelerate analyses, uncover patterns across vast datasets, and improve the accuracy and scalability of resilience assessments.

Preference is for someone who can begin by February 1, 2026.