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For Companies At a Glance
- Tasks: Join our Science Team to tackle geospatial engineering challenges and build scalable tools.
- Company: Climate X is dedicated to making a purposeful impact on climate change through innovative geospatial solutions.
- Benefits: Enjoy flexible hours, hybrid work, training budgets, and 36.5 days of holiday!
- Why this job: Make a real difference in understanding hazard dynamics while collaborating with experts in the field.
- Qualifications: PhD or equivalent experience in Earth Observation or geospatial data engineering required.
- Other info: Dog-friendly office with a Chief Mischief Officer and a supportive, inclusive culture.
The predicted salary is between 36000 - 60000 £ per year.
Geospatial Scientist | GDAL | Raster | Geospatial | Earth Observation | Python
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.
Essential Skills
- 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
Qualifications
- 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
💰 Pension contribution scheme
🏡 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)
Equal Opportunities
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.
Geospatial Scientist | GDAL | Raster | Geospatial | Earth Observation | Python
Geospatial Scientist | GDAL | Raster | Geospatial | Earth Observation | Python employer: Enigma
Contact Detail:
Enigma Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Geospatial Scientist | GDAL | Raster | Geospatial | Earth Observation | Python
✨Tip Number 1
Familiarize yourself with the specific geospatial tools and libraries mentioned in the job description, such as GDAL, xarray, and geopandas. Having hands-on experience with these technologies will not only boost your confidence but also demonstrate your readiness to tackle the challenges outlined in the role.
✨Tip Number 2
Engage with the geospatial community by participating in forums or attending relevant conferences. This can help you stay updated on the latest trends and best practices in geospatial science, which is crucial for a role that emphasizes innovation and leadership in the field.
✨Tip Number 3
Prepare to discuss your previous projects involving large geospatial datasets and how you optimized workflows. Be ready to share specific examples of how you solved complex problems, as this will showcase your practical experience and problem-solving skills during the interview.
✨Tip Number 4
Highlight any experience you have with cloud platforms like AWS, GCP, or Azure, especially in relation to geospatial data processing. Even if it's not a requirement, showing familiarity with these tools can set you apart from other candidates and align with the company's goals of scalable solutions.
We think you need these skills to ace Geospatial Scientist | GDAL | Raster | Geospatial | Earth Observation | Python
Some tips for your application 🫡
Highlight Relevant Experience: Make sure to emphasize your extensive experience with geospatial data engineering and model development. Include specific examples of projects where you processed large geospatial raster and vector datasets, particularly in Earth Observation.
Showcase Technical Skills: Clearly outline your proficiency in Python and the geospatial stack (xarray, numpy, scipy, dask, geopandas, OGR, GDAL). Mention any experience with AWS, GCP, or Azure for geospatial data storage and processing.
Demonstrate Problem-Solving Abilities: Provide examples of how you've solved complex geospatial engineering challenges. Discuss your approach to optimizing workflows and ensuring models are robust and scalable.
Express Passion for the Role: Convey your enthusiasm for the position and the impact it has on climate change. Share why you are interested in geospatial science and how you can contribute to the team's goals.
How to prepare for a job interview at Enigma
✨Showcase Your Technical Skills
Be prepared to discuss your experience with geospatial data engineering and model development. Highlight specific projects where you utilized Python and tools like GDAL, xarray, or geopandas to process large datasets.
✨Demonstrate Problem-Solving Abilities
Prepare examples of how you've tackled complex geospatial challenges in the past. Discuss your approach to optimizing workflows and ensuring models are robust and scalable.
✨Emphasize Collaboration and Leadership
Since this role involves working closely with hazard scientists, be ready to talk about your experience in collaborative environments. Share instances where you led initiatives or contributed to team success in geospatial projects.
✨Stay Updated on Industry Trends
Familiarize yourself with the latest advancements in Earth Observation and geospatial technologies. Being knowledgeable about current trends will demonstrate your passion for the field and your commitment to continuous learning.
