The Climate Action research area of the Alliance Bioversity-CIAT requires a Data Science Specialist with background, experience, and interest in using various modeling techniques to improve decision making for the agricultural sector in response to climate adaptation challenges. This position will design and implement decision support tools (DSTs) for improved agronomic management and climate adaptation for a variety of projects across the world. The successful candidate will leverage Alliance expertise in modeling and data science and use their own expertise to design new and improve existing DSTs to deliver climate-informed agronomic advisories at scale. The candidate will have a deep understanding of statistical models and crop models, including (but not limited to) process-based crop models, their development, calibration, and spatialization for agronomic adaptation applications. Further to this, a deep understanding of agronomy, crop physiology, and soil-plant-atmosphere interactions is essential for this position, since one of the tasks will be to tailor/translate crop modeling outputs into actionable climate-informed advisories for farmers. We seek somebody with a solution-oriented mindset and a collaborative spirit. The incumbent will work in a team of data scientists and modelers in Kenya, and in collaboration with scientists in other countries to understand users’ needs; and design, develop, and deploy DSTs, and coordinate activities and carry out capacity building activities with partners. The computational agronomist will leverage (and help build) expertise across the Alliance, coordinating with Alliance scientists from Asia and Latin America on related thematic areas.
The position will contribute to the development of tools and capacity development under the iSPARK (innovation in sustainability, policy, adaptation, and resilience in Kenya) project, and especially around the continued development, maintenance and scaling of digital tools that support the generation and delivery of site-specific recommendations for improved farmer decision making. The candidate will also be working as part of a variety of projects around agronomic advisory, including the Accelerating the Impacts of CGIAR Climate Research in Africa (AICCRA), helping build pathways for scaling climate-informed agronomic advisories.
Key Responsibilities:
Apply state-of-the-art data science methods to extract insights from field observations and geospatial data to provide climate smart and tailored agronomic recommendations at scale.
Take the lead on specific activities to modularize “white label” data analysis framework applying but not limited to process-based and empirical models to conduct research on yield prediction, improved technology targeting and impact modelling.
Support the connection of weather and climate forecasts into an agronomic recommendations engine / decision support system.
Contribute to conceptualization and development of robust yet practical decision support tools (DST) for the deployment of climate services and agronomic advisories within partners’ existing extension platforms.
Collaborate effectively with EiA TRANSFORM Work Package members, providing guidance and building capacity around crop modeling and its applications for generating agronomic gain.
Contribute to building and nurturing key partnerships in Africa working closely with NARs, Agricultural Services, Digital Partners, Farmer Organizations, and other CGIAR centers.
Coordinate activities internally at the Alliance and CGIAR and stay up to date on activities of key partners, as well as on new methods and approaches to crop modeling and climate services.
Contribute to and lead reporting, and scientific paper writing.
Participate in grant proposal writing and project management to secure funding for research projects deemed necessary and relevant.
Requirements
MSc or PhD in agronomy, soil science, agrometeorology, or any related fields
Strong problem-solving abilities and analytical thinking skills, with a demonstrated ability to analyze complex datasets, identify patterns, and derive actionable insights to inform decision-making.
Experience in training, and knowledge in process-based crop models such as DSSAT, APSIM, WOFOST.
Expert knowledge in applying machine learning techniques and algorithms, such as random forests, gradient boosting, support vector machines, neural networks, and ensemble methods to solve agronomic problems and optimize decision support systems.
Experience working with and/or developing crop model software in original model frameworks (e.g., C++, Fortran) is a plus.
Demonstrated ability to program in R and/or Python.
Demonstrated experience in designing, developing, and deploying automated and generic data analysis pipelines using programming languages such as Python and/or R and use of version control systems (e.g., Git).
Extensive experience of handling and processing gridded datasets relevant for spatial modeling such as weather, climate data, soil and demonstrated expertise in developing agronomic decision support tools to inform better farm management strategies.
Experience with making use of agile development methodologies, including iterative development, sprint planning, and continuous integration to maintain code integrity and documentation.
Experience working with partners from academia, NGOs, CGIAR centers, and national agricultural research centers in the context of smallholder farming systems in the lower- and middle-income countries.
Proficient in software development and IT infrastructure management, with demonstrated experience in packaging agronomic advisories into deployable solutions.
Strong knowledge of web development frameworks, cloud computing platforms, and version control systems to effectively deploy agronomic advisories on partners’ platforms while ensuring scalability, reliability, and security.
Excellent oral and written communication skills in English. French would be a plus.
Applications MUST include reference number 103645: Data Science Specialist – Climate Services and Agronomy as the position applied for
Apply via :
al.zohorecruit.eu
