Working in a small team you will be researching the availability and functionality of data sets that can be used in the development of new parametric solutions to address client needs. Such data might include satellite images, reanalysis model output, or station observations of perils such as floods, tropical cyclones, or wildfires. You will be analysing these data sets and using them to develop parametric solutions that can be deployed by clients and prospects globally. The role will involve supporting both our London team and our global teams.
You will need to have exceptionally strong data analytics, quantitative research, coding skills, and the ability to clearly articulate complex scientific concepts to colleagues and clients.
The role will include, to a lesser degree, client liaison, business development, solution structuring, and liaison with technical teams of partner insurers, reinsurers and other risk carriers.
- MSc/MSci degree (or equivalent) in Science, Technology, Engineering or Mathematics.
- Research or industry experience within any Earth Science discipline.
- An Earth Science degree with a strong mathematical and physical science component (e.g. Geophysics, Meteorology, Physical Oceanography) or a Mathematics degree with a strong statistical component.
- Previous research or industry experience with applications to renewable energy or natural hazards.
- Exceptionally strong data analytics, quantitative and scientific research skills.
- Previous experience working in an insurance or financial services environment.
- Inquisitive, adaptable and able to work independently.
- Excellent communications skills (both verbally and written).
- Proficiency in MS Office (Excel, Word and PowerPoint).
- The ability to break down a project and develop code to solve complicated problems without a known solution.
- Proficiency in geospatial mapping (e.g. QGIS).
- Proficiency in Python or R.
- An appreciation of how to apply statistical methods to real data (e.g. data cleaning, multi-linear regression, correlation analysis, Monte Carlo modelling).