Climate & Probabilistic Wildfire Risk Modeller (Fixed Term)

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 أموز отвутфنوفيرلاتنَ أัปال واحنشجاتorting risk insights and mitigation analyses across any landscape, anywhere in the world. We are currently developing wildfire risk грудиّ الباهات 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, analyzing 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 technicalIVAL, and academic advisors.

Dieses 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 modeling 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 modeling 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 Ph.D. in a relevant field.

Required:

• Strong BCM 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 modeling
• Demonstrated ability to document methods and explain modeling assumptions clearly (e.g., publications, reports, or technical documentation)
• Comfortable engaging with academic literature and translating it into practical modeling decisions
• Bayesian or hierarchical modeling
• Experience working with climate scenarios (e.g., IPCC context)
• Experience with wildfire, natural hazards, or catastrophe modeling

Timeline: সংগু 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

Seniority level

Entry level

Employment type

Contract

Job function

Design, Art/Creative, and Information Technology

Industries

Climate Data and Analytics