Summary
Date Posted: Monday, February 13, 2023 , Base Salary: Not Disclosed

JOB DETAILS:

Duties and responsibilities
Terms of Reference:
• Develop and implement spatial targeting efforts for agronomy investments
• Implement workflows for spatial predictions of yield responses to agronomy investments in target populations and geographies
• Design and supervise ex-ante impact analyses of agronomy project activities
• Development of dashboards and other means of enabling interactive information queries related to priority indicators
• Organize, manage, and analyze primary data collection through focus group discussions, key informant interviews, farm household surveys, and market surveys.
• Contribute to project reporting and preparation of scientific manuscripts to be published in high-impact, peer-reviewed journals.
• Build institutional networks and contacts for effective functioning of current project and for developing future collaborations


Requirements
Preferred academic qualifications, skills and attitudes:
• Advanced R programming skills: programming expertise in other languages (Python, JavaScript, Julia) and computational environments such as Google Earth Engine are an asset
• Familiarity with data visualization methods and interactive dashboards (e.g., R shiny apps) is highly desirable
• Demonstrated expertise with spatial data and spatial modeling; experience with remote sensing, geo statistics, and/or spatial econometrics are an asset
• Demonstrated expertise in machine learning prediction methods, as well as other branches of applied statistics, are essential; knowledge of econometrics is a strong asset
• Ability to integrate data from multiple sources (e.g., open data, crowd-sourcing, and remote sensing)
• Training in data science, geographic information science, computer programming, statistics or other relevant methods in the context of applied natural or social sciences
• Prior experience with collection, assembly, processing and visualization of large datasets to describe agricultural productivity patterns, cropping systems resilience and corresponding explanatory factors
• Proficiency in written and spoken English
• Knowledge of agronomy, soil science, climatology or agricultural economics is an asset
• Demonstrated familiarity with smallholder farming systems is an asset

Education, knowledge and experience
• Advanced R programming skills: programing expertise in other languages (Python, JavaScript, Julia) and computational environments such as Google Earth Engine are an asset
• Familiarity with data visualization methods and interactive dashboards (e.g., R shiny apps) is highly desirable
• Demonstrated expertise with spatial data and spatial modeling; experience with remote sensing, geostatistics, and/or spatial econometrics are an asset
• Demonstrated expertise in machine learning prediction methods, as well as other branches of applied statistics, are essential; knowledge of econometrics is a strong asset
• Ability to integrate data from multiple sources (e.g., open data, crowd-sourcing, and remote sensing)
• Training in data science, geographic information science, computer programming, statistics or other relevant methods in the context of applied natural or social sciences
• Prior experience with collection, assembly, processing and visualization of large datasets to describe agricultural productivity patterns, cropping systems resilience and corresponding explanatory factors
• Proficiency in written and spoken English
• Knowledge of agronomy, soil science, climatology or agricultural economics is an asset.
• Demonstrated familiarity with smallholder farming systems is an asset.


Job Experience: No Requirements

Work Hours: 8

Level of Education: Bachelor Degree

Job application procedure

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