Metals Quantitative Researcher: Signals & Trading

Qube Research and Technologies Switzerland AG

Genf

Vor Ort

CHF 140.000 - 200.000

Vollzeit

Vor 5 Tagen
Sei unter den ersten Bewerbenden
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Zusammenfassung

Qube Research and Technologies Switzerland AG is seeking a Quantitative Researcher - Metals to combine fundamental and quantitative research, translating supply and demand analysis into actionable trading signals, alongside modelling, dashboard development and trade execution.

You will work with large datasets to identify statistical patterns, share results with researchers, implement signals on the global platform and monitor model performance while leading the full cycle from signal generation

Qualifikationen

  • Advanced degree in a quantitative field.
  • Strong knowledge in statistics, machine learning, NLP or AI.
  • Experience coding in Python, R, Matlab, C++, or C#.
  • Ability to work autonomously in a fast-paced, global team.
  • Experience analyzing large datasets across multiple time frames is a plus.

Aufgaben

  • Create high quality predictive signals.
  • Identify statistical patterns and opportunities from large datasets.
  • Share and discuss results, methodology, datasets and processes with other researchers.
  • Implement signals and datasets within the global execution platform.
  • Monitor signal behaviour and model performance over time.
  • Lead the full strategy research cycle from signal generation to implementation.

Kenntnisse

Quantitative degree
Statistics
Machine learning
NLP/AI
Python
R
Matlab
C++
C#
Dataset exploration
Data analysis
Communication
Autonomy

Ausbildung

Bachelor’s degree from a university of applied sciences or equivalent

Tools

Python
R
Matlab
C++
C#

Jobbeschreibung

Qube Research and Technologies Switzerland AG is seeking a Quantitative Researcher - Metals to combine fundamental and quantitative research, translating supply and demand analysis into actionable trading signals, alongside modelling, dashboard development and trade execution.

You will work with large datasets to identify statistical patterns, share results with researchers, implement signals on the global platform and monitor model performance while leading the full cycle from signal generation

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