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

• Develop and implement best practices and protocols for data cleaning, analysis and modeling.
• Design and oversee the development of complex statistical models for agronomic data analysis.
• Implement machine learning and AI techniques to extract insights from large-scale agricultural datasets.
• Validate and refine models to ensure accuracy and reliability of results.
• Lead the development of methodologies for calculating key agricultural KPIs, including:Greenhouse Gas emissions
• Soil health indices
• Sustainable practice adoption rates
• Ensure alignment of KPI calculations with international standards and best practices.
• Collaborate with breeding data teams to integrate data-driven insights into agronomic analyses.
• Implement data governance practices to ensure data integrity and security.
• Oversee the development and maintenance of data pipelines and storage systems.
• Prepare and present comprehensive reports on analytical findings to stakeholders
• Develop data visualizations and dashboards to communicate complex information effectively
• 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.

The application deadline is 29 August 2024