ABOUT US

At Climate X, we’re building global-scale models to assess the physical risks of climate change. We are seeking a scientist with a strong background in applied statistics and physical loss modelling. This role is ideal for someone experienced with natural catastrophe models and comfortable working with large geospatial and financial loss datasets. We combine observation data and model outputs at both local and global scales to project physical impacts of climate change, such as floods, subsidence, storms, droughts, and wildfires. You will be a key contributor to the development, calibration and validation of loss models that support our mission to help clients assess and manage climate risks.

ESSENTIAL SKILLS

• Prior experience in building and calibrating loss models using statistical and mathematical modelling approaches, ideally across multiple perils.
• Proficiency working with geospatial data (climate and Earth Observation) and observed or modelled economic damage datasets.
• Strong programming skills in Python/R or another language.
• Strong communication skills with ability to explain complex concepts to a non-technical audience.
• Ability to work independently and contribute to a fast-moving research and development environment.

DESIRABLE SKILLS

Experience in any one of these areas makes you stand out but are not required for the role and if you are passionate and interested in the role we encourage you to apply!

• Expertise in catastrophe modelling, especially in the exposure and vulnerability domain.
• Applied statistical skills, including uncertainty quantification, Bayesian statistics and/or Extreme Value Theory.
• Machine learning knowledge with applications in climate risk assessment.
• Experience with cloud computing (e.g. AWS/Google Cloud)

QUALIFICATIONS

• PhD or equivalent experience in climate science, hazard modelling, statistical analysis and remote sensing.
• Minimum of 3 years of industry experience in statistical or catastrophe modelling.

• Expertise in catastrophe modelling, especially in the exposure and vulnerability domain.
• Applied statistical skills, including uncertainty quantification, Bayesian statistics and/or Extreme Value Theory.
• Machine learning knowledge with applications in climate risk assessment.
• Experience with cloud computing (e.g. AWS/Google Cloud