Data Scientist (Electromagnetic Environment)
Data Scientist in London applying machine learning and data science to electromagnetic environment problems (spectrum classification, emitter detection, signal characterisation) within Metrea's Decision Science Laboratory, taking research from experimentation through to deployed operational capabilities. Core stack: Python, ML frameworks, MLOps and cloud infrastructure.
- Compensation
- Not disclosed
- Location
- London, England
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Full time
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Post-doctoral degree in a quantitative field (Physics, Engineering, Mathematics, Computer Science, Statistics, or related STEM), or equivalent professional experience supported by a Master's degree
- Familiarity with modern ML approaches such as CNNs, Transformers, autoencoders, self-supervised learning, probabilistic modelling, or simulation-driven learning
- Strong programming capability in Python, including scientific computing, machine learning frameworks, and data analysis libraries
- Experience applying data science, machine learning, or advanced analytics to electromagnetic or related operational problem sets
- Ability to design rigorous experiments, evaluate model and algorithm performance, and translate findings into clear technical recommendations
- Excellent communication skills, explaining complex ML, RF, statistical, and engineering concepts to technical and non-technical audiences
- Collaborative mindset, strong technical curiosity, and ability to work in a fast-paced, mission-focused environment
- Eligible to hold UK SC-level security clearance
- Right to live and work in the UK
Full description
Company Overview
Metrea delivers effects-as-a-service to national security partners across five domains and more than a dozen mission areas. These include airborne ISR, electronic warfare, secure communications, aerial refueling, special air missions, aerial firefighting, and advanced simulation. We own the whole stack: designing, building, and operating turnkey capabilities that give our partners decisive, asymmetric advantage against rapidly evolving threats.
Our operating model is built around three interlocking pillars. The Support Groups provide a global shared-service – spanning people, finance, platform, operations, legal, and engagement. This frees up our Core Groups, who develop and own mission capabilities end-to-end, to focus entirely on delivery. The Market Groups apply a regional lens, ensuring that our agile and adaptable capabilities remain aligned to the wicked problems that matter most to our partners across the Americas, EMEA, and Asia-Pacific.
At the heart of our model is a simple but powerful idea: be a true partner with skin in the game. Our partners need effects, not just equipment. By owning the full stack – from the lab to the field – we are able to drive a continuous cycle of innovation that keeps our partners ahead. It's a fast-moving, intellectually demanding environment where talented people are given real responsibility, work on problems that matter, and contribute to an enterprise that is growing quickly and deliberately.
Headquartered in Washington, DC, with facilities across the United States, the United Kingdom, as well as Continental Europe and Asia-Pacific.
Group Overview
The Digital and Synthetic Capability Unit (D&S) is committed to providing mission-driven information solutions that seamlessly bridge the digital and physical realms. Leveraging cutting-edge technologies and advanced platforms, we empower operational readiness and elevate situational awareness across diverse domains—including air, maritime, and space.
Position Summary
In this role, you will apply machine learning, data science, and advanced analytical techniques to complex challenges across the electromagnetic environment. Working within Metrea’s Decision Science Laboratory (DESLAB) and automated Solution Engine, you will design, test, and deploy ML-enabled capabilities that go beyond traditional signal processing approaches. This includes exploring the potential of methods such as Transformers, self-supervised learning, probabilistic modelling and simulation-driven learning approaches for electromagnetic sensing and exploitation. You will be successful if you are comfortable moving between research, experimentation, and operational delivery, turning ambiguous Defence and Aerospace problems into robust technical solutions that can be tested, explained, and deployed in real-world settings.