Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Develop and validate loss models to assess climate-related risks like floods and storms.
- Company: Join a forward-thinking company focused on global-scale climate risk modelling.
- Benefits: Enjoy hybrid work options and a competitive salary of up to £80,000.
- Why this job: Make a real impact on climate change while working in a dynamic R&D environment.
- Qualifications: Experience in statistical modelling, programming skills in Python or R, and knowledge of geospatial data required.
- Other info: This role does not offer sponsorship.
The predicted salary is between 48000 - 64000 £ per year.
Scientist – Loss Modelling (Physical Risk from Weather and Climate)
London – Hybrid
Up to £80,000
About the Role
Our client is building global-scale models to assess the physical risks of climate change and is looking for a Scientist with expertise in applied statistics and physical loss modelling.
This role is ideal for someone experienced in natural catastrophe modelling and confident working with large geospatial and financial loss datasets.
You’ll contribute to the development, calibration, and validation of loss models that project climate-related physical impacts such as floods, subsidence, storms, droughts, and wildfires — helping clients assess and manage climate risks effectively.
Key Responsibilities
- Develop loss models to quantify climate-related risks, with a focus on estimating asset vulnerabilities.
- Calibrate and validate models using a wide range of qualitative and quantitative data sources.
- Contribute to building a robust and adaptable loss modelling framework.
- Document methodologies and communicate scientific insights to stakeholders, clients, and at industry events and conferences.
What We’re Looking For
- Experience building and calibrating statistical/mathematical loss models, ideally across multiple perils.
- Proficiency with geospatial data (climate and Earth observation) and economic damage datasets.
- Strong programming skills in Python, R, or a similar language.
- Excellent communication skills, able to explain complex concepts to non-technical audiences.
- A self-starter who thrives in a fast-paced R&D environment.
- Experience in catastrophe modelling, especially around exposure and vulnerability.
- Applied statistical skills such as Bayesian statistics, uncertainty quantification, or Extreme Value Theory.
- Knowledge of machine learning applications in climate risk.
- Familiarity with cloud platforms (e.g., AWS, Google Cloud).
If this role looks of interest, please apply below.
Please note – this role does not offer sponsorship.
Loss Modeller employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Loss Modeller
✨Tip Number 1
Familiarise yourself with the latest advancements in climate risk modelling. Attend webinars or workshops related to natural catastrophe modelling and geospatial data analysis to stay updated and network with industry professionals.
✨Tip Number 2
Showcase your programming skills by working on relevant projects or contributing to open-source initiatives. This will not only enhance your coding abilities but also demonstrate your practical experience with Python, R, or similar languages.
✨Tip Number 3
Prepare to discuss your experience with statistical methods, particularly Bayesian statistics and Extreme Value Theory. Be ready to explain how you've applied these techniques in past projects, as this will highlight your expertise in loss modelling.
✨Tip Number 4
Practice explaining complex concepts in simple terms. Since excellent communication skills are essential for this role, consider doing mock presentations to friends or colleagues to refine your ability to convey technical information to non-technical audiences.
We think you need these skills to ace Loss Modeller
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in building and calibrating statistical loss models. Emphasise your proficiency with geospatial data and programming skills in Python or R, as these are crucial for the role.
Craft a Compelling Cover Letter: In your cover letter, explain why you are passionate about climate risk modelling. Mention specific projects or experiences that demonstrate your expertise in natural catastrophe modelling and your ability to communicate complex concepts effectively.
Showcase Relevant Skills: Clearly outline your applied statistical skills, such as Bayesian statistics or Extreme Value Theory, in your application. Highlight any experience with machine learning applications in climate risk, as this will set you apart from other candidates.
Prepare for Technical Questions: Be ready to discuss your methodologies and insights during potential interviews. Prepare examples of how you've developed, calibrated, and validated loss models, and be prepared to explain these processes to a non-technical audience.
How to prepare for a job interview at Harnham
✨Showcase Your Technical Skills
Be prepared to discuss your experience with statistical and mathematical loss models. Highlight specific projects where you've built or calibrated models, especially those related to climate risks. This will demonstrate your expertise and relevance to the role.
✨Demonstrate Your Data Proficiency
Since the role requires working with geospatial and financial datasets, be ready to talk about your proficiency in handling such data. Mention any tools or programming languages you’ve used, like Python or R, and provide examples of how you've applied them in past projects.
✨Communicate Complex Concepts Clearly
Given the need to explain complex ideas to non-technical audiences, practice simplifying your explanations. Prepare to discuss how you would communicate your findings to stakeholders, ensuring they understand the implications of your models without getting lost in technical jargon.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Think about how you would approach calibrating a model for a specific climate-related risk, and be ready to outline your thought process and methodologies.
