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Consultant- Spatial Data Scientist

Reference number: 202308
Job status: Inactive
Job category: Consultancy
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

Consultant- Spatial Data Scientist

Overview

The consultant will conduct analysis in support of research-for-development activities under the Excellence in Agronomy initiative of the One CGIAR. Key aspects of this work include development of reproducible workflows for spatial targeting of agronomy investments in candidate areas of priority geographies distributed globally, as well as predictive analytics around impacts of agronomic investments at scale.

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.

Terms and conditions

  • •Submit a technical and financial proposal which should include a description of the proposed methodology to be used and a schedule of planned activities. •Submit a detailed CV •Submit three professional references

Application process

The application deadline is 28 Feb-2023
We will acknowledge all applications, but will contact only short-listed candidates.

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