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Closing: Jun 9, 2022

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Published: May 12, 2022 (12 days ago)

Job Requirements

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Job Summary

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Qualifications and Experience
 MSc. Degree in GIS, Environmental science, Agronomy, Geo-spatial information
science.
 Have a strong background in use of Python specifically for spatial data science and
modelling, experience in basic crop science, and a passion for building solutions to
difficult social problems in agriculture sector.

 Have a minimum of Three (3) years’ experience in industry or applied research and
be eager to apply those skills to emerging challenges in agriculture.
 Three or more years working in geo-spatial data science industry or applied research
in academia.
 Profound experience with supervised machine learning algorithms (both deep
learning and non-deep learning approaches) and with both structured and
unstructured data (e.g., tabular administrative data, satellite imagery, text, hard-copy
data etc).
 Knowledge and ability to use Linux servers, deploy/run applications on Linux

environments.

 Experience in mentoring other staff and training stakeholders, including the ability

and interest in sharing knowledge, mentoring others, presenting work and reviewing

the work of peers.

 Experience with the design and delivery of a geo-spatial tool or product from start to

finish.

 Experience in use of cloud computation platforms.

 Experience working with satellite imagery / remote sensing data for agriculture.

 Ability to analyse statistical data using SPSS, Stata or R. In principle, ability to mine

and discover knowledge using intelligent algorithms.

 Experience in conducting fieldworks to collect or validate spatial predictions. In
addition, the ability to develop customized field collection applications using opensource solutions.
 Ability to use computer vision algorithms for agriculture applications.

 Ability to apply state-of-the-art data science skills to enable integration and
processing of large datasets from multiple sources.
 Experience with programming in teams, and methods to maintain code integrity,
documentation and standards.
 Ability to develop and/or refine existing scripts and tools for statistical analysis tha
can be used to curate, analyse and visualize their data


Responsibilities
Qualifications and Experience
 MSc. Degree in GIS, Environmental science, Agronomy, Geo-spatial information
science.
 Have a strong background in use of Python specifically for spatial data science and
modelling, experience in basic crop science, and a passion for building solutions to
difficult social problems in agriculture sector.

 Have a minimum of Three (3) years’ experience in industry or applied research and
be eager to apply those skills to emerging challenges in agriculture.
 Three or more years working in geo-spatial data science industry or applied research
in academia.
 Profound experience with supervised machine learning algorithms (both deep
learning and non-deep learning approaches) and with both structured and
unstructured data (e.g., tabular administrative data, satellite imagery, text, hard-copy
data etc).
 Knowledge and ability to use Linux servers, deploy/run applications on Linux

environments.

 Experience in mentoring other staff and training stakeholders, including the ability

and interest in sharing knowledge, mentoring others, presenting work and reviewing

the work of peers.

 Experience with the design and delivery of a geo-spatial tool or product from start to

finish.

 Experience in use of cloud computation platforms.

 Experience working with satellite imagery / remote sensing data for agriculture.

 Ability to analyse statistical data using SPSS, Stata or R. In principle, ability to mine

and discover knowledge using intelligent algorithms.

 Experience in conducting fieldworks to collect or validate spatial predictions. In
addition, the ability to develop customized field collection applications using opensource solutions.
 Ability to use computer vision algorithms for agriculture applications.

 Ability to apply state-of-the-art data science skills to enable integration and
processing of large datasets from multiple sources.
 Experience with programming in teams, and methods to maintain code integrity,
documentation and standards.
 Ability to develop and/or refine existing scripts and tools for statistical analysis tha
can be used to curate, analyse and visualize their data


 Support the institution’s crop modelling work, including implementation of three
crop models and their anticipated scaling.

 Support big data mining using intelligent algorithms, advanced coding and statistical
analysis for complex data synthesis and analytics for several countries in 2022 and 11 countries in 2023.
 Develop required mechanisms for automation and data analytics.

 Assist in establishing capabilities around growing data science needs and propel the
region’s capacity in the use of AI, deep learning/machine learning, and crop models.
 Work with stakeholders, governments, and agricultural scientists in the region.


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