13439- Data Analyst Programmer
| Dyddiad hysbysebu: | 08 Rhagfyr 2025 |
|---|---|
| Cyflog: | £34,610 i £39,906 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 05 Ionawr 2026 |
| Lleoliad: | Edinburgh, Scotland |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 3 ddiwrnod yr wythnos |
| Cwmni: | University of Edinburgh |
| Math o swydd: | Dros dro |
| Cyfeirnod swydd: | 13439 |
Crynodeb
Grade UE06: £34, 610- £39,906 per annum
Royal (Dick) School of Veterinary Studies
Full Time: 35 hours per week
Fixed Term: 42 months
The Opportunity
We are seeking a methodological, rigorous and service-oriented individual to support data processing in SEBI-L and provide data support services to deliver to the Gates Foundation. The post-holder will write code that is scalable, tested and validated to process project data.
This post is full-time (35 hours per week) and we are open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.
The post holder is expected to attend our weekly in-person team meetings at Easter Bush Campus. The post is available on a 42-month fixed-term contract basis.
SEBI-Livestock is a dynamic and innovative organisation tasked with improving data and evidence in low- and middle-income countries to support better informed decision making. We provide an extensive data driven monitoring and learning service to the Gates Foundation in tracking their livestock investments. In addition, we convene a large livestock data network, Livestock Data for Decisions (LD4D) to help achieve common goals.
The postholder will work on building a scalable, tested and validated data pipeline to process livestock and aquaculture data.
This role will play a crucial part in supporting the SEBI-L team in transforming and importing livestock data into the SEBI-L platform.
Responsibilities will include:
The writing, running and maintenance of scripts to automate and test data processing workflows;
Ensuring code is version controlled, validated and robust in collaboration with other team members;
Supporting the automating and running of livestock models and analyses.
Within this role, there is an opportunity to develop data visualization skills and to work on projects in Machine Learning and Natural Language Processing.
This position is based within SEBI-Livestock, the Centre for Supporting Evidence Based Interventions in Livestock, which is hosted by the Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh.
SEBI-Livestock offers a collaborative, fast-paced environment with opportunities for learning, growth and the chance to contribute to addressing real-world challenges.
Your skills and attributes for success:
Educated to degree level in a numerical discipline such as Bioinformatics, Computer Science, Mathematics or equivalent;
Demonstrable experience of R programming and modular code;
Basic knowledge and experience of Python and SQL databases;
Good knowledge and experience of data science technologies and of good software engineering practices including use of version control and implementation of unit and integration tests;
An interest in exploring the application of new technologies including Natural Language Processing and Machine Learning
Royal (Dick) School of Veterinary Studies
Full Time: 35 hours per week
Fixed Term: 42 months
The Opportunity
We are seeking a methodological, rigorous and service-oriented individual to support data processing in SEBI-L and provide data support services to deliver to the Gates Foundation. The post-holder will write code that is scalable, tested and validated to process project data.
This post is full-time (35 hours per week) and we are open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.
The post holder is expected to attend our weekly in-person team meetings at Easter Bush Campus. The post is available on a 42-month fixed-term contract basis.
SEBI-Livestock is a dynamic and innovative organisation tasked with improving data and evidence in low- and middle-income countries to support better informed decision making. We provide an extensive data driven monitoring and learning service to the Gates Foundation in tracking their livestock investments. In addition, we convene a large livestock data network, Livestock Data for Decisions (LD4D) to help achieve common goals.
The postholder will work on building a scalable, tested and validated data pipeline to process livestock and aquaculture data.
This role will play a crucial part in supporting the SEBI-L team in transforming and importing livestock data into the SEBI-L platform.
Responsibilities will include:
The writing, running and maintenance of scripts to automate and test data processing workflows;
Ensuring code is version controlled, validated and robust in collaboration with other team members;
Supporting the automating and running of livestock models and analyses.
Within this role, there is an opportunity to develop data visualization skills and to work on projects in Machine Learning and Natural Language Processing.
This position is based within SEBI-Livestock, the Centre for Supporting Evidence Based Interventions in Livestock, which is hosted by the Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh.
SEBI-Livestock offers a collaborative, fast-paced environment with opportunities for learning, growth and the chance to contribute to addressing real-world challenges.
Your skills and attributes for success:
Educated to degree level in a numerical discipline such as Bioinformatics, Computer Science, Mathematics or equivalent;
Demonstrable experience of R programming and modular code;
Basic knowledge and experience of Python and SQL databases;
Good knowledge and experience of data science technologies and of good software engineering practices including use of version control and implementation of unit and integration tests;
An interest in exploring the application of new technologies including Natural Language Processing and Machine Learning