Cervest is an ambitious start-up with a mission to do good – we are trying to fix some of the problems that the Earth and humankind face with regard to sustainability, and climate risk. It’s a big challenge, and it’s why we’ve built a research team with an impressive track record in climate science, statistics, machine learning and AI.
Focus of the role:
The science team at Cervest combines expertise in statistics, computer science, data engineering, machine learning, physics and remote sensing with one goal: to make Earth Science decision-useful for people and organisations to plan for the future. This starts with the quantification of a broad range of physical climate risks, both short- and long-term. We are looking to augment our team with specific expertise in processing, analysing and interpreting weather and climate data at scale. We are looking for candidates who are excited to combine their scientific and software engineering knowledge with our machine learning and engineering teams to help us build the necessary data pipelines and democratise weather and climate data.
We are a pro-diversity company and passionate about bringing together people of all backgrounds, because we know that a diverse team will help us achieve our mission sooner.
What you’ll be doing – main responsibilities
- Build robust, maintainable, scalable systems to manage weather and climate data;
- Collaborate with ML scientists to extract meaningful features from weather data;
- Lead the specification of scientifically informed statistical models;
- Participate in decisions regarding Cervest’s science and product roadmap;
- Engage in an open and collaborative scientific environment within Cervest and its external collaborators from academia and industry;
- Publish and present your scientific and engineering work internally and externally.
Great if you have:
- 5+ years of professional experience and/or have advanced degree in a quantitative field, ideally in the Earth Sciences
- Solid software engineering skills
- Experience with GIS and handling geospatial data
- Experience handling meteorological or climatic raster data (e.g. NetCDF or GRIB)
- Academic or professional expertise in atmospheric physics, meteorology or climate science
- Interest in solving Earth science problems using AI
Even better if you have:
- Experience with ECMWF and CORDEX data products
- Experience with scientific computing and global circulation models
- Experience writing software in industry or for a collaborative open source project
What’s in it for you:
Salary – £50K – 60K / annum (dependent on experience)
Opportunities to learn, grow and thrive with support from talented and empathetic team mates
This role will be mainly remote for at least the remainder of 2020 and potentially longer-term; however you must be able to work core UK office hours, so between 8-4 or 10-6. Our working language is English.
Fuller list of benefits on our main career page – we’re an early stage startup and currently reviewing our benefits in light of becoming a remote-first company. We are committed to ensuring that we support our team in developing in line with their aspirations and talents as well as continuing to develop our culture in line with our values.
For a confidential, informal discussion on Cervest and the role, please feel free to contact Dr Maxime Rischard at firstname.lastname@example.org. Alternatively please send your CV and a brief cover letter directly to Maxime at the same email address. We do not respond to cold outreach from recruitment agencies.
The interview process for this role will be approximately 7-8 hours in total (which we accommodate as much as possible around your obligations), comprising four stages:
- An initial call with the team lead (30-60 minutes)
- Mostly non-technical interviews with members of the team and elsewhere in the business (2-3 hours)
- A technical test which you can do in your own time or as a pairing exercise, followed by a discussion of the test (2 hours)
- Final interviews with our CEO Iggy and one-two other members of the leadership team (1-2 hours)