Data Scientist (Electromagnetic Environment)
84674Th44Metrea9999 LondonEst. Est. GBP 65,000–85,000 / yearMid
Estimated range based on role, country and industry — not published by the company.
Key requirements
- Python
- Machine Learning
- Data Science
- Agile
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.
What You'll Do
Translate operational requirements into technical solutions, working closely with cross-functional teams across decision science, electromagnetic simulation, waveform design, engineering, operations, hardware, and aerial systems
Apply machine learning, data science, and RF-aware analytical techniques to electromagnetic, problem sets, including spectrum classification, emitter detection, anomaly detection and signal characterisation
Explore and develop modern approaches for electromagnetic sensing and exploitation, including convolutional neural networks, transformers, autoencoders, graph-based methods or probabilistic approaches for uncertainty-aware decision-making
Design and conduct experiments to assess new algorithms and techniques
Develop proof-of-concept capabilities and support the deployment of selected approaches into operational environments
Liaise with forward-deployed teammates to understand real-world constraints, user needs, data limitations, environmental effects, and deployment considerations
Build reliable analytical workflows using appropriate MLOps , and cloud-ba
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