CIFOR-ICRAF
Under the supervision of the head of SPACIAL the Spatial Data Scientist – Remote Sensing will lead
and implement advanced remote sensing analysis tasks, including processing of optical and SAR
data, timeseries analysis, and predictive modeling with primary focus on the modeling of temporal
dynamics across complex landscapes. The position will support a range of projects and programmes
across CIFOR-ICRAF, including Regreening Africa, Knowledge for Great Green Wall Action (K4GGWA).
Towards Ending Drought Emergencies (Twende), and upcoming assessments of soil and land health
with funding from NORAD.
Duties and responsibilities
- Design and implement machine learning pipelines for geospatial analysis, including feature engineering, model selection, hyper parameter tuning, and validation.
- Develop and deploy deep learning models (CNNs, RNNs, LSTMs, Transformers) for image classification, segmentation, object detection, and time series forecasting.
- Apply advanced AI techniques for predictive modelling and mapping of indicators relevant to ecosystem health assessment using field data and multi-source remote sensing.
- Process and analyze optical data (Sentinel 2, Landsat 8/9) and SAR data (Sentinel 1), including data fusion and feature extraction for ML workflows.
- Implement time series analysis and forecasting models, including trend detection, anomaly identification, and predictive analytics for vegetation, precipitation, and land surface dynamics.
- Develop scalable, reproducible spatial data processing workflows and contribute to MLOps practices.
- Supervise a team of junior spatial data scientists and developers. • Develop communication products/outputs where relevant.
- Lead internal capacity development seminars within CIFOR-ICRAF on machine learning, AI applications, and spatial data science.
- Capacity development of partners and stakeholders through workshops as part of projects with particular emphasis on ML-driven spatial analysis and modelling.
- Work closely with the CIFOR-ICRAF stakeholder engagement team (SHARED) to provide AI-driven 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.
- 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
- PhD or MSc degree in spatial data science, geoinformatics, computer science, or a related quantitative field with demonstrated expertise in machine learning and AI applications.
- Proven experience developing and deploying machine learning models for geospatial applications.
- Strong proficiency in deep learning frameworks (TensorFlow, PyTorch, Keras) and familiarity with architectures such as CNNs, RNNs, LSTMs, and Transformers.
- Advanced programming skills in Python and/or R Statistics; familiarity with Julia is a plus.
- Experience with cloud computing platforms (GEE, AWS, GCP) and big data processing tools for geospatial analysis.
- Knowledge of remote sensing data processing and analysis, including optical and SAR platforms.
- Excellent interpersonal skills.
- Excellent written and spoken English. Knowledge of French a plus.
Education, knowledge and experience
• PhD or MSc degree in spatial data science, geoinformatics, computer science, or a related
quantitative field with demonstrated expertise in machine learning and AI applications.
• Proven experience developing and deploying machine learning models for geospatial applications.
• Strong proficiency in deep learning frameworks (TensorFlow, PyTorch, Keras) and familiarity with
architectures such as CNNs, RNNs, LSTMs, and Transformers.
• Advanced programming skills in Python and/or R Statistics; familiarity with Julia is a plus.
• Experience with cloud computing platforms (GEE, AWS, GCP) and big data processing tools for
geospatial analysis.
• Knowledge of remote sensing data processing and analysis, including optical and SAR platforms
Terms and conditions
• This is a Globally Recruited Staff (GRS) position. CIFOR-ICRAF offers competitive remuneration in
USD, commensurate with skills and experience.
• The appointment will be for two (2) year period, inclusive of a six-month probationary period, with
the possibility of extension contingent upon performance, continued relevance of the position and
available resources.
• The duty station will be in Kenya, Nairobi or Remote.