You will need to be a naturally curious person with an analytical mindset and leadership experience.
You will have a logical approach to problem solving, be comfortable with working with large datasets of various forms and will understand Environmental/Earth Science concepts. You will also need to be familiar with GIS and ideally catastrophe modelling and/or natural hazard mapping.
You will be able to work with a range of colleagues with differing knowledge and backgrounds, be flexible in response to changing priorities, and not be phased by novel concepts.
You will lead a highly qualified team of scientists working on QC of software, data and scientific methods. You will not be shy to challenge concepts or to demand robust justification of proposed methods.
Flood-specific experience is less important than a scientifically questioning approach, evidence of strong transferrable skills and the ability to learn quickly.
You will need to able to demonstrate the following essential attributes:
- Higher research degree in a scientific discipline or equivalent
- Experience of line management, managing priorities and work allocation/resourcing
- Professional manner with strong interpersonal skills
- Demonstrable ability to understand and clearly communicate technical concepts orally and in written English
- Experience of teamwork and collaboration across boundaries both in person and via collaboration technologies
- Understanding of the computational data processing environment, common software and systems used
- Experience of spatial data and GIS
- Experience using numerical or statistical modelling methods
- Coding experience in a high-level language using source control e.g. Python, R, SQL.
- Competent using MS Office 365 suite of tools
If you have any of the following desirable attributes, even better:
- Experience of software testing, UAT
- Use of software development and testing tools Familiar with Linux
- Experience with test automation frameworks, particularly for user interfaces
- LEAN/SixSigma, experience or formal qualifications
- An understanding of statistical concepts