Data Scientist for Climate Services and Agronomy Decision Support at International Institute of Tropical Agriculture

• IITA is hiring a data scientist with background, experience, and interest in using various statistical and modeling techniques to improve decision making for the agricultural sector. This position will contribute strongly to the design and implementation of decision support tools (DSTs) for improved agronomic management and climate adaptation for a variety of projects. The successful candidate will leverage expertise across CGIAR in modeling and data science. The candidate will have a deep understanding of statistical models and, ideally, crop models. Further to this, a deep understanding of agronomy and soil-plant-atmosphere interactions is essential for this position since one of the tasks will be to tailor/translate modeling outputs into actionable 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 position will contribute to tools and capacity development under the iSPARK (Innovation in Sustainability, Policy, Adaptation and Resilience in Kenya) project, 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.

Position 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
• Lead 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
• Work closely with climate information service experts and software engineers to integrate seasonal crop forecasts to optimize planting dates and dynamic agronomic advisories
• 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 all iSPARK project partners, 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
• Contribute to the development and maintenance of scalable, user-friendly digital platforms for delivering climate-informed agronomic advisories, ensuring seamless integration with partners' systems and adherence to industry standards and best practices
• Collaborate with cross-functional teams to identify technical requirements, design robust architectures, and implement efficient deployment pipelines, facilitating the timely dissemination of climate-informed agronomic advisories to end-users in diverse agricultural contexts
• 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

• The candidate will ideally have a PhD degree in data science, crop or climate modeling, bioinformatics, biostatistics, or agronomy/soil science with strong computational skills. However, highly qualified candidates without a PhD are strongly encouraged to apply

Core Competencies

• At least two years of data analytics and decision support tools development experience in agricultural applications
• Demonstrated proficiency in statistical modeling techniques
• Strong problem-solving abilities and analytical skills, with demonstrated ability to analyze complex datasets, identify patterns, and derive actionable insights to inform decision-making
• Expert knowledge in applying machine learning techniques and algorithms, such as random forest, gradient boosting, support vector machines, neural networks, and ensemble methods to solve agronomic problems and optimize decision support systems
• Strong R and Python programming skills, with demonstrated experience in designing, developing, and deploying automated and generic data analysis pipelines using these programming languages
• Strong demonstrated ability to implement best practices in documenting and sharing code, including 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 lower-and middle-income countries
• 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
• Experience with or domain knowledge of agriculture and agricultural data and the ability to apply these in developing decision support highly desirable
• Excellent oral and written communication skills in English and French would be a plus