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Quantitative Research Intern (Hong Kong/Remote)

Remote · USA Full-time New today

This a Full Remote job, the offer is available from: Asia

  • This is a fully remote internship position, candidates based in Hong Kong or other cities in Asia are preferred. Please note that we don't have physical office in HK*

• Internship can start at your preferred date upon discussion with the team. For students who don't reside in Asia, please note that you may be required to interview and conduct sync-up meetings in Asia hours*

Job Description

We are looking for Quant Researcher intern that has strong coding skills (Python, Golang or C++), a demonstrable curiosity about mid/long term frequency trading, quantitative trading, and some knowledge of cryptocurrency trading. The role will involve a significant amount of research and development when testing and asset trader on developing new trading strategies. You will also be heavily involved in researching, implementing, and executing strategies, and automating them where applicable.

Requirements

  • An MSc / PhD in a research-intensive STEM field (Computer Science, Statistics, Mathematics, Physics, Engineering etc)
  • Degree from a top-tier university
  • A passion for developing quantitative trading strategies as well as analyzing and drawing conclusions from large datasets
  • Self-motivated and strong research mindset
  • You are required to finish several research projects independently with guidance from the team during this internship period
  • Strong coding skills essential, 2 to 5 years of experience coding in Python, Golang,or C++ is required (test required)
  • The candidate must have completed at least a 3-month internship or full-time position at a proprietary trading firm or hedge fund as a Quantitative Researcher/Trader, with prior involvement in alpha-related projects during the internship (school projects are not considered, nor is experience as a data analyst).

Desirable Skills

  • Experience with Machine Learning, Natural Language Processing or Deep Learning
  • Knowledge of the DeFi and Web 3.0 ecosystem

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