Core
Early-career research scientist owning end-to-end applied machine-learning research problems, from framing questions to building validated, defensible solutions on real-world data.
Role type
Early-career applied machine-learning research scientist
Builds
Validated machine-learning models and reproducible evaluation harnesses for real-world problems
Domain
Applied machine learning, data science, quantitative research
Deliverable
production ML models
Required skills
Python, first-principles algorithm implementation, data cleaning and quality assurance, out-of-sample evaluation, leakage detection, technical communication, independent problem-solving
Preferred skills
PyTorch, compiled/systems languages, causal inference, time-series modelling, Bayesian methods, numerical optimisation, signal processing, cross-disciplinary background
Technologies
Python, PyTorch
Responsibilities
Frame and solve open-ended applied ML problems end-to-end; build and validate models on messy real-world data; implement methods from first principles; design reproducible evaluation harnesses; communicate results to technical and non-technical audiences; collaborate in a high-density team to improve team processes
Seniority
Early-career, hands-on IC
