Cervest is building the world’s first open access AI-powered Climate Intelligence platform.

We’re a certified B-Corp with a vision to democratize access to Climate Intelligence driving a shared responsibility to protect the world’s critical assets — including our greatest shared asset, the planet.

It’s an exciting time to join us. We’ve just raised $30m in Series A investment. Our inaugural product, EarthScan™, will launch in 2021. EarthScan™ enables organizations to de-risk business decisions, meet financial disclosure guidelines, improve resilience, and uncover new opportunities to accelerate low-carbon growth.

We’re backed by leading venture capital firms, including Draper Esprit, Future Positive, Lowercarbon Capital, and Astanor Ventures. We are now building up our team in all areas: sales, marketing, science, engineering, and people operations.

As a company, we are a pro-diversity, highly inclusive organization, committed to bringing together people of all backgrounds and enabling them to succeed. We know that a richly diverse team will help us achieve our mission sooner.

As a Statistical Scientist at Cervest, you will:

  • Build and evaluate ML pipelines for Earth Science modelling, such as weather and climate downscaling, extreme events modelling, or physical risk estimation.
  • Write clean, easy-to-understand code, and contribute to the team’s software engineering knowledge.
  • Communicate complex scientific concepts simply but non-reductively to other teams and clients.
  • Collaborate with designers and engineers to ensure that Science Team output is incorporated into the product as smoothly and optimally as possible.
  • Read scientific papers to understand the current state-of-the-art for relevant modelling tasks, and attempt to replicate those papers where appropriate.


  • Statistical, Machine Learning, or Deep Learning background, with industry experience.
  • Solid software engineering skills, particularly relating to data engineering.
  • Good scientific communication skills, able to explain technical concepts to a non-technical audience.
  • Pragmatic approach to problem solving.
  • Experience with Python.
  • Experience working with geospatial data, ideally climate or weather data.

Bonus points for:

  • Experience with Julia
  • Experience in geospatial statistics, modelling physical systems, extreme value analysis, scalable Bayesian inference.
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