At a Glance
- Tasks: Join us as a Scientist to tackle geospatial engineering challenges and build scalable tools.
- Company: We're a leading climate risk provider, backed by Google Ventures, based in London and New York.
- Benefits: Enjoy flexible hours, hybrid working, 25 days holiday, and a monthly training budget.
- Why this job: Make a real impact on climate change while developing your skills in a supportive environment.
- Qualifications: Strong experience with geospatial datasets and Python; PhD or equivalent preferred.
- Other info: Dog-friendly office with a Chief Mischief Officer and fun quarterly socials!
The predicted salary is between 36000 - 60000 £ per year.
We are a leading global climate risk and analytics provider located in London, UK and New York, US. Earlier this year, we closed Series A led by GV (Google Ventures) and are on a fast trajectory to lead the financial services and asset management industries with our B2E SaaS platform, comprised of three products, Carta, Spectra and Adapt, which resolve customer climate risk challenges across their entire workflows. We are at a pivotal point in scaling our product offering, expanding our market reach, and refining our product strategy to sustain continued success.
About the role
We are seeking a Scientist with extensive experience in geospatial data engineering and model development (physical, statistical or machine learning) and with expertise in Earth Observation processing techniques and sources. You will need to be adaptable to tackle technical challenges across geospatial and physical modelling domains. As a key member of the Science Team, you will be pivotal in building scalable geospatial tools, supporting hazard scientists in complex modelling tasks, creating high-quality input datasets, and future-proofing our processes to scale to new geographies and advanced methodologies.
The impact you’ll own:
- Solve geospatial engineering challenges: Solve problems by optimising workflows for massive global geospatial datasets: from processing terabytes of satellite imagery to scaling out a river flood model on a continental scale. You’ll help ensure that models are robust, high-performance, and able to scale seamlessly.
- Champion best practices: Shape the way geospatial science gets done by creating smarter, faster approaches —whether it’s building reusable tools to validate our models or designing systems to ensure every model run is reproducible. You’ll lead the charge in making geospatial work seamless, ensuring the team can focus on solving big-picture challenges.
- Build new geospatial datasets: Take ownership of transforming diverse datasets into cohesive, standardised formats, whether it’s compiling observations of wildfire occurrence, or harmonising tropical cyclone and storm surge data for detailed hazard modelling. You’ll transform raw data into analysis-ready, cloud-optimised datasets tailored for large-scale geospatial processing.
- Geospatial leadership: Collaborate with hazard scientists, enhancing the complexity, precision, and scope of their modelling tasks. Enable accurate, data-driven insights for a deeper understanding of hazard dynamics across diverse environments.
Qualifications:
- Strong experience working with large geospatial raster and vector datasets (e.g. Earth Observation or climate datasets).
- Experience with processing of large and complex datasets in a python based geospatial stack (xarray, numpy, scipy, dask, geopandas, OGR, GDAL).
- Strong numerical skills with the ability to apply quantitative methods to extract meaningful insights from spatial data.
- Familiar with modelling concepts from one of hydrology, atmospheric, environmental sciences.
Desirable skills:
- Experience in any one of these areas makes you stand out but are not required for the role and if you are passionate and interested in the role we encourage you to apply!
- Experienced with AWS, GCP, or Azure for geospatial data storage, access, and distributed computation.
- Experience processing optical or radar Earth Observation datasets.
- Experience building geospatial machine learning or physical/semi-empirical models.
- PhD or equivalent experience in Earth Observation, geospatial data engineering or environmental sciences.
- Minimum of 3 years industry experience in geospatial domain.
Benefits:
- Contribute to a business making purposeful impact related to climate change.
- Monthly training & conference budget to help you upskill and develop your career (£1,000 per year).
- 6 monthly appraisals and 12 monthly pay reviews.
- Flexible hours and hybrid working (3 days/week in office; core hours 10am-4pm).
- Mental Health and Wellbeing support via Oliva.
- 25 days holiday, plus Bank Holidays, annual 3-day Christmas-closure, and half day on your birthday (36.5 days total!).
- Optional quarterly socials, dinners, and fun nights out.
- A fully stocked supply of snacks, fruit, and refreshments for the days when you are in the office.
- Cycle to work scheme via gogeta.
- Enhanced maternity and paternity.
- Pawternity leave.
- Dog friendly office (official residence of Alfie, Chief Mischief Officer).
Climate X are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to creating an inclusive environment for all employees and welcome applications from individuals of all backgrounds.
How to apply: Visit our careers page, and come talk to us at EGU!
Scientist (Geospatial Specialist) employer: European Geosciences Union
Contact Detail:
European Geosciences Union Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Scientist (Geospatial Specialist)
✨Tip Number 1
Familiarise yourself with the specific geospatial tools and technologies mentioned in the job description, such as xarray, numpy, and GDAL. Being able to discuss your hands-on experience with these tools during an interview will demonstrate your technical expertise and readiness for the role.
✨Tip Number 2
Showcase your problem-solving skills by preparing examples of how you've optimised workflows or tackled complex geospatial challenges in previous roles. This will help you illustrate your ability to contribute to the team’s goals effectively.
✨Tip Number 3
Engage with the latest trends and advancements in Earth Observation and climate data processing. Being knowledgeable about current developments can give you an edge in discussions and show your passion for the field.
✨Tip Number 4
Network with professionals in the geospatial and climate science communities, especially those who may have insights into the company or similar roles. Building connections can provide valuable information and potentially lead to referrals.
We think you need these skills to ace Scientist (Geospatial Specialist)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with geospatial data engineering and model development. Emphasise your familiarity with Earth Observation processing techniques and any relevant projects you've worked on.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about climate risk and analytics. Discuss how your skills align with the role's requirements, particularly in optimising workflows for large geospatial datasets.
Showcase Relevant Projects: Include specific examples of past projects where you solved geospatial engineering challenges or built new geospatial datasets. This will demonstrate your practical experience and problem-solving abilities.
Highlight Continuous Learning: Mention any recent training, conferences, or courses you've attended that relate to geospatial science or climate change. This shows your commitment to staying updated in the field and your eagerness to grow professionally.
How to prepare for a job interview at European Geosciences Union
✨Showcase Your Technical Skills
Be prepared to discuss your experience with large geospatial datasets and the specific tools you've used, such as Python libraries like xarray, numpy, and geopandas. Highlight any projects where you optimised workflows or processed complex datasets.
✨Demonstrate Problem-Solving Abilities
Think of examples where you've tackled technical challenges in geospatial modelling. Be ready to explain your thought process and how you approached these problems, especially in relation to climate data and hazard modelling.
✨Emphasise Collaboration and Leadership
Since the role involves working closely with hazard scientists, share experiences where you've collaborated on projects. Discuss how you contributed to team success and any leadership roles you've taken in guiding geospatial science practices.
✨Prepare for Scenario-Based Questions
Expect questions that assess your adaptability and creativity in solving geospatial engineering challenges. Prepare to discuss hypothetical scenarios related to processing satellite imagery or building new datasets, showcasing your analytical skills and innovative thinking.