Take ownership of applied machine-learning research problems end to end: framing the question, assembling and cleaning the data, choosing and implementing the method, evaluating it honestly, and reaching a defensible conclusion.
Build and validate models on messy, real-world data, with rigorous attention to data quality, data leakage, regime change, and genuine out-of-sample performance.
Implement methods from first principles where needed, in Python and, where appropriate, a compiled or systems language.
Design evaluation harnesses and benchmarks so that your own results, and the team's, are reproducible and falsifiable rather than merely asserted.
Communicate your reasoning and results clearly to both technical and non-technical colleagues, and independently reproduce and pressure-test teammates' results.
Work collaboratively in a small, high-density team, and raise the way the team works, not only your own output.

Location: Genève, Rue du Rhône 5/7/9, 1204 Genève
Employer: Brevan Howard Investment Products Limited, Saint-Hélier (Jersey), succursale de Genève

Research Scientist

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