Climate & Probabilistic Wildfire Risk Modeller

Pinepeak Ltd.; Remote; GBP 40,000 - 60,000; Full time

Job summary

A technology company dedicated to climate resilience is seeking a Climate & Probabilistic Wildfire Risk Modeller to deliver a probabilistic wildfire risk assessment as part of a project. This role requires strong foundations in probability and statistics, proficiency in Python, and experience with climate datasets. The position is fully remote with flexible hours. Compensation is negotiable based on experience. Starting ASAP and concluding by the end of May 2026.

Qualifications

• Strong foundations in probability and statistics.
• Experience implementing statistical models.
• Comfortable engaging with academic literature.

Responsibilities

• Deliver a probabilistic wildfire risk assessment.
• Work with large-scale geospatial datasets.
• Produce clear documentation of methodology.

Skills

Probability and statistics foundations

Experience with climate and geospatial datasets

Proficiency in Python

Ability to document methods clearly

Bayesian or hierarchical modelling

Experience with wildfire modelling

Education

Master's or PhD in relevant field

Job description

We are a Cambridge spin-out on a mission to make communities safer and more climate-resilient with next-generation wildfire data and analytics. Our physics-driven, AI-enhanced software for wildfire propagation, combined with probabilistic modelling and climate science, produces transparent and defensible wildfire risk insights and mitigation analyses across any landscape, anywhere in the world. We are currently developing wildfire risk assessments for under-represented and data-challenged regions, where wildfire impacts can be severe and existing analytical coverage is limited.

The Role

We are seeking a Climate & Probabilistic Wildfire Risk Modeller to help deliver a large-scale wildfire risk assessment as part of a time-bound project. You will work hands‑on in the implementation of probabilistic wildfire models, analysing climate, environmental, and geospatial datasets to produce well‑documented, defensible outputs. Standard methods for data‑rich regions will not always apply, and the role will involve supporting the development and adaptation of approaches suited to data‑scarce and challenging contexts. You will work closely with Pinepeak’s founders, interdisciplinary technical team, and academic advisors.

This role offers the opportunity to apply and further hone your expertise in catastrophe and climate risk modelling, including probabilistic analysis, geospatial AI, and Earth Observation data, while contributing to forward‑looking climate analysis and reusable methodologies. Candidates whose background is primarily in software engineering, platform development, or application engineering (without substantial modelling experience) are unlikely to be a good fit for this role.

Your Mission

• Help deliver a probabilistic wildfire risk assessment within a fixed timeframe
• Work with climate, environmental, and large‑scale geospatial datasets in data‑scarce contexts
• Implement and adapt modelling approaches where standard methods are insufficient
• Contribute to a forward‑looking view of wildfire hazard under future climate conditions, with explicit treatment of uncertainty
• Produce clear documentation explaining methodology, assumptions, uncertainty, and limitations

Skills & Background

We are open to candidates from a range of quantitative backgrounds, typically with a Master’s or PhD in a relevant field.

Required:

• Strong foundations in probability and statistics, with experience implementing probabilistic and/or statistical models (ideally in the context of risk assessment)
• Experience working with climate, environmental, and/or geospatial datasets
• Strong proficiency in Python for scientific computing and statistical modelling
• Demonstrated ability to document methods and explain modelling assumptions clearly (e.g., publications, reports, or technical documentation)
• Comfortable engaging with academic literature and translating it into practical modelling decisions
• Bayesian or hierarchical modelling
• Experience working with climate scenarios (e.g., IPCC context)
• Experience with wildfire, natural hazards, or catastrophe modelling

Timeline

Starting ASAP, concluding by the end of May 2026

Location

Fully remote with flexible working hours; occasional on‑site days at our Central London office possible

Compensation

Flexible and commensurate with experience; daily or monthly rates negotiable