10006 - Data Scientist
| Posting date: | 25 April 2024 |
|---|---|
| Salary: | £37,099 to £44,263 per year |
| Hours: | Full time |
| Closing date: | 20 May 2024 |
| Location: | Edinburgh, Scotland |
| Remote working: | On-site only |
| Company: | University of Edinburgh |
| Job type: | Temporary |
| Job reference: | 10006 |
Summary
UE07 £37,099 to £44,263 per annum (A revised salary range for this grade of £39,347 to £46,974 is planned to take effect from Spring 2024)
School of Informatics
Contract Type - 36 Months - with the possibility of extension
Full Time - 35 Hours Per Week
Applications are invited for a Data Scientist in the Data Science Unit in the School of Informatics, University of Edinburgh, with a focus on Data Science for Science, Health, People and Environment.
The Opportunity:
You will work with the Lead Data Scientist and other Data Scientists and researchers at the Unit in raising awareness of data science methods and practices within the University, influencing academic and researcher colleagues to engage with advanced data science tools in their research, applying cutting-edge data science tools to solve real-world problems for social good, and developing and delivering novel and innovative computational solutions to practical problems in the wider context of, but not limited to, science, health, people and environment.
You will be working on multiple interdisciplinary projects on a variety of datasets with collaborators from other disciplines, developing innovative ways to foster interdisciplinarity, and will play a key role in establishing collaboration and engagement between School of Informatics and the other schools. You will facilitate and support the uptake of state-of- the-art data science methods to make a significant contribution to advancing research at the University and at all three colleges, i.e., College of Science and Engineering, College of Arts, Humanities and Social Sciences, and College of Medicine and Veterinary Medicine. You will develop novel statistical machine learning tools in collaboration with domain experts to facilitate more principled, robust and unbiased use of data in deriving knowledge, and disseminate these outcomes to the broader research community.
This full-time position (35 hours per week), is fixed term for 36 months in the first instance, with the possibility of extension.
Your skills and attributes for success:
Masters degree in a quantitative subject (e.g. Artificial Intelligence, Statistics).
Significant experience (typically a minimum of 3 years) in a role involving data science.
Expertise in managing, structuring, and analysing various kinds of data.
Expertise in developing, implementing, and deploying statistical methods and machine learning models.
Expertise in one or more statistical languages (R, Python) with experience in writing documentation.
Excellent verbal communication skills including the ability to present technical information to non-technical audience, and engaging stakeholders.
Excellent written communication skill including writing structured technical reports and/or scientific publications.
Experience of working on multiple projects simultaneously and managing workload and deliverables.
Willingness to continue to learn new techniques, and openness to address new problem presented by domain experts.
School of Informatics
Contract Type - 36 Months - with the possibility of extension
Full Time - 35 Hours Per Week
Applications are invited for a Data Scientist in the Data Science Unit in the School of Informatics, University of Edinburgh, with a focus on Data Science for Science, Health, People and Environment.
The Opportunity:
You will work with the Lead Data Scientist and other Data Scientists and researchers at the Unit in raising awareness of data science methods and practices within the University, influencing academic and researcher colleagues to engage with advanced data science tools in their research, applying cutting-edge data science tools to solve real-world problems for social good, and developing and delivering novel and innovative computational solutions to practical problems in the wider context of, but not limited to, science, health, people and environment.
You will be working on multiple interdisciplinary projects on a variety of datasets with collaborators from other disciplines, developing innovative ways to foster interdisciplinarity, and will play a key role in establishing collaboration and engagement between School of Informatics and the other schools. You will facilitate and support the uptake of state-of- the-art data science methods to make a significant contribution to advancing research at the University and at all three colleges, i.e., College of Science and Engineering, College of Arts, Humanities and Social Sciences, and College of Medicine and Veterinary Medicine. You will develop novel statistical machine learning tools in collaboration with domain experts to facilitate more principled, robust and unbiased use of data in deriving knowledge, and disseminate these outcomes to the broader research community.
This full-time position (35 hours per week), is fixed term for 36 months in the first instance, with the possibility of extension.
Your skills and attributes for success:
Masters degree in a quantitative subject (e.g. Artificial Intelligence, Statistics).
Significant experience (typically a minimum of 3 years) in a role involving data science.
Expertise in managing, structuring, and analysing various kinds of data.
Expertise in developing, implementing, and deploying statistical methods and machine learning models.
Expertise in one or more statistical languages (R, Python) with experience in writing documentation.
Excellent verbal communication skills including the ability to present technical information to non-technical audience, and engaging stakeholders.
Excellent written communication skill including writing structured technical reports and/or scientific publications.
Experience of working on multiple projects simultaneously and managing workload and deliverables.
Willingness to continue to learn new techniques, and openness to address new problem presented by domain experts.