Reference number: 202440
Job status: In-progress
Job category: Local Position
Duty station: Nairobi, Kenya
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CIFOR-ICRAF
The Center for International Forestry Research (CIFOR) and World Agroforestry (ICRAF) envision a more equitable world where trees in all landscapes, from drylands to the humid tropics, enhance the environment and well-being for all.
CIFOR and ICRAF are non-profit science institutions that build and apply evidence to today’s most pressing challenges, including energy insecurity and the climate and biodiversity crises. Over a combined total of 65 years, we have built vast knowledge on forests and trees outside of forests in agricultural landscapes (agroforestry).
Using a multidisciplinary approach, we seek to improve lives and to protect and restore ecosystems. Our work focuses on innovative research, partnering for impact, and engaging with stakeholders on policies and practices to benefit people and the planet.
Founded in 1993 and 1978, CIFOR and ICRAF are members of CGIAR, a global research partnership for a food secure future dedicated to reducing poverty, enhancing food and nutrition security, and improving natural resources.
CIFOR-ICRAF is looking for a
Data Specialist – Agronomic Analytics and Modeling CIMMYT
Overview
This post will contribute to both bilateral projects requiring data analytics and ONECGIAR initiatives including Harnessing digital technologies (DX1) and AgriLAC Resiliente. This is a position within the MEAL team funded both by SAS project portfolio, as well as OneCGIAR initiatives and bilaterals in Asia (Bihar) and Africa (USAID).
Duties and responsibilities
1. Develop and implement best practices and protocols for data cleaning, analysis and modeling.
2. Design and oversee the development of complex statistical models for agronomic data analysis.
3. Implement machine learning and AI techniques to extract insights from large-scale agricultural datasets.
4. Validate and refine models to ensure accuracy and reliability of results.
5. Lead the development of methodologies for calculating key agricultural KPIs, including:
· Greenhouse Gas emissions
· Soil health indices
· Sustainable practice adoption rates
6. Ensure alignment of KPI calculations with international standards and best practices.
7. Collaborate with breeding data teams to integrate data-driven insights into agronomic analyses.
8. Implement data governance practices to ensure data integrity and security.
9. Oversee the development and maintenance of data pipelines and storage systems.
10. Prepare and present comprehensive reports on analytical findings to stakeholders
11. Develop data visualizations and dashboards to communicate complex information effectively
12. Collaborate with cross-functional teams to translate analytical insights into actionable recommendations.
Requirements
- Ph.D. in Data Science, Statistics, Computer Science, or a related field.
- Minimum 5 years of experience in leading data science teams, preferably in an agricultural or environmental context.
- Strong background in statistical analysis, machine learning, and predictive modeling.
- Proficiency in programming languages such as R, Python, and SQL.
Education, knowledge and experience
•Ph.D. in Data Science, Statistics, Computer Science, or a related field.
•Minimum 5 years of experience in leading data science teams, preferably in an agricultural or environmental context.
•Strong background in statistical analysis, machine learning, and predictive modeling.
•Proficiency in programming languages such as R, Python, and SQL.
Terms and conditions
•This is a Locally Recruited Staff (LRS) position and on competitive remuneration in local currency, commensurate with skills and experience.
•The appointment will be for a period of two (2) years, 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 Nairobi, Kenya.
Application process
The application deadline is 29 Aug 2024
We will acknowledge all applications, but will contact only short-listed candidates.
