Quant Researcher - Mixed-Frequency Trading & ML (HK)

Jump Trading

Hong Kong

Presencial

HKD 800 000 - 1 200 000

Tempo integral

14 dias+

Uma candidatura completa num minuto

Currículo e carta de apresentação personalizados, prontos a enviar.

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Resumo da oferta

Jump Trading Group, a leader in quantitative research for global equity stat arb, is expanding a new Hong Kong-based team. Researchers will develop and apply mixed-frequency strategies, leveraging ML, statistics, and data engineering to forecast markets and build predictive models.

You will collaborate with traders, engineers, and researchers across disciplines to push innovative ideas in a fast-paced, supportive environment. Advanced degrees in related fields are highly valued.

Qualificações

  • Experience with quantitative trading strategies is preferred.
  • Forecasting and machine learning techniques such as linear models or neural networks are used.
  • Ability to collaborate with traders, researchers, and engineers in a fast-paced environment.

Responsabilidades

  • Collect and analyze large data sets to identify patterns in global financial markets.
  • Apply statistical analysis and ML techniques to develop predictive trading models.
  • Collaborate with scientists, traders, hardware and software engineers to push ideas forward.

Conhecimentos

Quantitative research
Machine learning
Data analysis
C++/Python
Statistical modeling
Team collaboration

Formação académica

Bachelor/Master/PhD in CS/Stats/Physics/Math

Ferramentas

C++
Python
Linux

Descrição da oferta de emprego

Jump Trading Group, a leader in quantitative research for global equity stat arb, is expanding a new Hong Kong-based team. Researchers will develop and apply mixed-frequency strategies, leveraging ML, statistics, and data engineering to forecast markets and build predictive models.

You will collaborate with traders, engineers, and researchers across disciplines to push innovative ideas in a fast-paced, supportive environment. Advanced degrees in related fields are highly valued.

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