Scientist (Loss modelling: physical risk from weather and climate) - #1744267

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.

The impact you'll own

• Loss Model Development: Research and implement loss models that quantify climate-related risks, focusing on robustly estimating asset vulnerabilities.
• Model Calibration & Validation: Calibrate and validate loss models against a wide variety of data sources, both qualitatively and quantitatively to ensure they meet rigorous scientific standards.
• Framework Development: Contribute to building our overall loss modelling framework, ensuring robustness and adaptability across diverse client requirements and geographies.
• Disseminate scientific insights and methods through model documentation and presentations to engage with stakeholder and customers, all while representing the team at international conferences and industry events.
  
  

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.
  
  

Benefits

Contribute to a business making purposeful impact related to climate change

Monthly training & conference budget to help you upskill and develop your career (£1,000 per year)

6 monthly appraisals and 12 monthly pay reviews

Pension contribution scheme

Flexible hours and hybrid working (3 days/week in office; core hours 10am-4pm)

Mental Health and Wellbeing support via Oliva

25 days holiday, plus Bank Holidays, annual 3-day Christmas-closure, and half day on your birthday (36.5 days total!)

Optional quarterly socials, dinners, and fun nights out

A fully stocked supply of snacks, fruit, and refreshments for the days when you are in the office

Cycle to work scheme via gogeta

Enhanced maternity and paternity

Pawternity leave

Dog friendly office (official residence of Alfie, Chief Mischief Officer)

Equal Opportunities

Climate X are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to creating an inclusive environment for all employees and welcome applications from individuals of all backgrounds.