Backend Developer Specialist - Weather Risk - Financial Inclusion in Africa
Budget: £250 – £750 GBP
The overall goal of DAFS is to enhance access to finance by smallholder farmers and promote their participation in crop value chains. The DAFS project will: (i) promote crop insurance to smallholder farmers by minimizing climate related supply chain disruptions; and (ii) improve financing and investment to the agricultural sector through credit guarantee schemes. In this regard, DAFS will pilot and upscale a new parametric insurance that will create the opportunity for mutual incentives to smallholder farmers, banks and insurance companies, with the view to unlocking investment at scale. The project will be up-scaled in Tanzania, and pilots will run in two additional countries including Uganda and Zimbabwe.
The project will introduce a new financial de-risking tool within the banking sector of the benefitting countries, with the objective of systematically increasing access to finance for agriculture and smallholder farmers in each of the target countries. DAFS is structured in four complementary components: (i) research and development of risk modelling; (ii) development and introduction of blended credit guarantee and insurance de-risking schemes, (iii) capacity building for small-scale farmers and financial institutions; and (iv) project management and coordination.
Background
The Consultant will be responsible for adapting and streamlining the implementation of different backend algorithms to run of different server platforms and scale as needed. This role involves extensive work with algorithms that integrate various types of data and models including meteorological and climate data, crop type recognition, and hydrological modelling. The ideal candidate will have a strong background in computer science and experience with parallel computing using both CPU and GPU on a cluster environment.
Core Responsibilities
• Adapt algorithms for meteorological data and climate model data downloading, formatting. The algorithms should be executable on a cloud parallel environment through a simple command with arguments that can be called through a graphical user interface.
• Implement a) hydrological model, b) crop models, c) land type recognition, d) weather indices computation, algorithms to run flexibly on a cloud environment and to be executed through a simple command with all arguments necessary to be called through a graphical user interface. The algorithms are written in Python, R, and NCL. A “readme” file documenting the codes to enable reproducibility will be also produced.
• Ensure that each component of the pipeline (e.g., hydrological model, crop models, etc) can be executed independently and using command line interface with all needed arguments (e.g., selection of input files, values of simulation parameters) that can be subsequently selected and called through a graphical user interface.
• Create containers and environments (e.g., Conda-based) for each component of the pipeline such including 1/ crop model, 2/ hydrological model, 3/ weather indices computation, 4/ risk analysis computation, 5/ climate model data downloading and processing, 6/ high-resolution satellite image recognition, 7/ input and output file visualization algorithms.
• Design and devise the orchestration of the full containerized pipeline through Kubernetes to enable portability of the pipeline across platforms.
Create a database structure to enable users to access an isolated work space (e.g., either a simplified or advanced version of the interface and modules/containers, and access to specific profiles)
Qualifications
• Proficiency in Python, R, C++, SQL or similar.
• Experience with containerization technology and Kubernetes.
• Strong experience in Unix/Linux systems operations.
• Strong understanding of parallel computing architectures (CPU and GPU).
• Ability to work collaboratively in a team environment and communicate technical concepts effectively.
• Ability to follow a workplan and adhere to a work timeline
The project will introduce a new financial de-risking tool within the banking sector of the benefitting countries, with the objective of systematically increasing access to finance for agriculture and smallholder farmers in each of the target countries. DAFS is structured in four complementary components: (i) research and development of risk modelling; (ii) development and introduction of blended credit guarantee and insurance de-risking schemes, (iii) capacity building for small-scale farmers and financial institutions; and (iv) project management and coordination.
Background
The Consultant will be responsible for adapting and streamlining the implementation of different backend algorithms to run of different server platforms and scale as needed. This role involves extensive work with algorithms that integrate various types of data and models including meteorological and climate data, crop type recognition, and hydrological modelling. The ideal candidate will have a strong background in computer science and experience with parallel computing using both CPU and GPU on a cluster environment.
Core Responsibilities
• Adapt algorithms for meteorological data and climate model data downloading, formatting. The algorithms should be executable on a cloud parallel environment through a simple command with arguments that can be called through a graphical user interface.
• Implement a) hydrological model, b) crop models, c) land type recognition, d) weather indices computation, algorithms to run flexibly on a cloud environment and to be executed through a simple command with all arguments necessary to be called through a graphical user interface. The algorithms are written in Python, R, and NCL. A “readme” file documenting the codes to enable reproducibility will be also produced.
• Ensure that each component of the pipeline (e.g., hydrological model, crop models, etc) can be executed independently and using command line interface with all needed arguments (e.g., selection of input files, values of simulation parameters) that can be subsequently selected and called through a graphical user interface.
• Create containers and environments (e.g., Conda-based) for each component of the pipeline such including 1/ crop model, 2/ hydrological model, 3/ weather indices computation, 4/ risk analysis computation, 5/ climate model data downloading and processing, 6/ high-resolution satellite image recognition, 7/ input and output file visualization algorithms.
• Design and devise the orchestration of the full containerized pipeline through Kubernetes to enable portability of the pipeline across platforms.
Create a database structure to enable users to access an isolated work space (e.g., either a simplified or advanced version of the interface and modules/containers, and access to specific profiles)
Qualifications
• Proficiency in Python, R, C++, SQL or similar.
• Experience with containerization technology and Kubernetes.
• Strong experience in Unix/Linux systems operations.
• Strong understanding of parallel computing architectures (CPU and GPU).
• Ability to work collaboratively in a team environment and communicate technical concepts effectively.
• Ability to follow a workplan and adhere to a work timeline