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.