Duties and responsibilities

Spatial data science

• Optical data processing and analysis, including from sensors such as Sentinel 2 and Landsat 8/9.
• SAR data processing and analysis, primarily Sentinel 1.
• Timeseries analysis, including detection of trends and anomalies in temporal data (vegetation, precipitation, land surface temperatures, etc)
• Predictive modeling of time series data, including forecasting where relevant.
• Applications of machine learning for predictive modeling and mapping of indicators relevant to assessment of ecosystem health using a combination of field data and remote sensing.
• Spatial data analysis tasks, including spatial queries, vector data extraction, processing, and cleaning.
• General data analysis and report writing.
• Supervise a team of junior spatial data scientists and developers.
• Develop communication products/outputs where relevant.

Capacity development

• Lead internal capacity development seminars within CIFOR-ICRAF.
• Capacity development of partners and stakeholders through workshops as part of projects with particular emphasis on spatial data processing and modeling.

Stakeholder engagement

• Work closely with the CIFOR-ICRAF stakeholder engagement team (SHARED) to provide analytical outputs that feed into project delivery, for example monitoring outputs as part of the Great Green Wall.
• Contribute to stakeholder engagement events as part of the development of decision support tools and platforms.

Various other tasks

• Contribute to micro-dashboard development as part of the Global Resilience Impact Tracker platform
• Support projects and programs with analytical support and stakeholder engagement with decision makers.
• Lead and/or contribute to scientific papers.
• Contribute to proposal development and writing.

Requirements

Education, knowledge and experience

• MSc degree in remote sensing and spatial data science is required with proven ability to conduct highly complex analytical tasks.
• Excellent interpersonal skills.
• Excellent written and spoken English.
• Advanced computer programming skills in R Statistics, some knowledge of Python and/or Julia, database technologies, and data management.