Revolutionising environmental risk analytics, one model at a time

A pioneering environmental analytics company is seeking an exceptional Environmental Risk Modeller to join their dynamic team and drive innovative climate and nature risk solutions. The successful candidate will play a crucial role in developing cutting-edge modelling techniques that help international corporations navigate the complex challenges of environmental sustainability.

This rapidly expanding organisation has already established partnerships with globally recognised brands including Nestlé, Burberry, and Reckitt. Their sophisticated SaaS platform enables businesses to assess and strategically manage environmental risks, supporting their transition to net-zero and more sustainable practices.

The company culture is built on intellectual curiosity, collaborative innovation, and a commitment to evidence-based problem-solving. They value diverse perspectives and welcome individuals who are unintimidated by complexity. Their ethos centres on respecting every team member's contribution, leaving egos aside, and working collectively towards transformative solutions. Alongside the basic salary, on offer are competitive bonus and pension schemes as well as flexible working arrangements and a genuine commitment to staff diversity and inclusion.

The role demands a senior quantitative modeller who can lead technical model development for climate and nature risk assessment. The ideal candidate will build geospatial data layers, develop innovative risk assessment techniques, and transform complex scientific insights into financially relevant business intelligence.

Requirements:

* As a minimum a Bachelor's degree in natural sciences, physics, engineering or equivalent and demonstrate relevant post-graduation experience; ideally relevant postdoctoral research experience.

* Have in-depth knowledge of mathematical modelling methods for environmental risk and their applications to real world problems.

* Possess extensive experience with analysis, manipulation and visualisation of climate, environmental, and geospatial datasets (CMIP6, SSP, etc).

* Demonstrate advanced proficiency in scientific programming (Python preferred).

* Exhibit exceptional communication skills for translating complex scientific concepts.

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