Job Quantitative Research Intern en Remote

Kronos Research en Hong Kong

Digital job Quantitative Research Intern at Kronos Research

Quantitative Research Intern

Kronos Research Hong Kong

$50,000 - $70,000

Remote Full-time
Development 2-5 años Testing Cryptocurrency Web Engineering
By Remote Ok
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Job description

*This is a fully remote term-time internship, candidates based in Hong Kong are preferable. Please note that we don't have physical office in HK*

*Internship can start at any time and there are not specific onboarding date.*

 

Job Description

We are looking for Quant Trader 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 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
  • 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

 

Salary

For candidates outside of Asia, the salary will be determined based on Asian standards rather than the candidate's local living cost.

 



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