EM Environment Data Scientist — ML for RF Sensing

Data Scientist (Electromagnetic Environment); Greater London; Hybride; GBP 90 000 - 120 000; Plein temps

Avantages offerts par ce poste

Private Medical Insurance

Gym membership plan

Electric Car scheme

Cycle to work scheme

Free lunch on office days

Enhanced Parental Leave

30 days annual PTO

Résumé du poste

Metrea is seeking a Data Scientist to apply machine learning and advanced analytics to the electromagnetic environment. You will work within Metrea’s Decision Science Laboratory (DESLAB) and the automated Solution Engine to design, test, and deploy ML-enabled capabilities beyond traditional signal processing.

You will explore transformers, probabilistic models, and simulation-driven learning, moving between research and operational delivery to solve Defence and Aerospace challenges.

Qualifications

• Master’s degree in a quantitative STEM field.
• Familiarity with CNNs, Transformers, autoencoders, and self-supervised learning.
• Strong Python programming and ML/data analysis experience.

Responsabilités

• Translate operational requirements into technical solutions with cross-functional teams.
• Apply ML, data science, and RF-aware analytics to EM problems.
• Explore modern approaches for electromagnetic sensing and exploitation.

Connaissances

Electromagnetic signal processing

Python programming

Machine learning

Data analysis

Experiment design

Communication skills

Formation

Master’s degree in STEM (Physics, Engineering, Mathematics, CS, Statistics)

Outils

CNNs

Transformers

Autoencoders

Self-supervised learning

Probabilistic modelling

Simulation-driven learning

Description du poste

Metrea is seeking a Data Scientist to apply machine learning and advanced analytics to the electromagnetic environment. You will work within Metrea’s Decision Science Laboratory (DESLAB) and the automated Solution Engine to design, test, and deploy ML-enabled capabilities beyond traditional signal processing.

You will explore transformers, probabilistic models, and simulation-driven learning, moving between research and operational delivery to solve Defence and Aerospace challenges.