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You are fascinated by financial markets and trading. You love reading papers, articles, and books about quantitative finance topics. You are excited by big data, machine learning, statistics, and applied mathematics. You are also ninja at coding algorithms and using various frameworks and libraries.



  • 1 year of C, C++, Linux experience

  • 1-2 years of quantitative research, machine learning, or statistics using Python, Matlab, R.

  • Bachelors or above in the following fields:  computer science, electrical engineering, financial mathematics, physics, statistics, applied mathematics.

  • IOI, IMO, IPhO, ACM, President's Award.


  • Become a machine learning domain expert.

How to Apply

  • Please email your resume to

  • Please upload your resume to Dropbox as a PDF

  • Save the PDF name in lower case as <chinese_name>.<english_last_name>.<english_first_name>.pdf

The Experience

AI & Machine Learning

Machine learning at our firm is not a gimmick, it is how we make money. Therefore, unlike most consumer facing industries, machine learning is treated as a profit center not as a cost center. We are also in one of the few industries where we have more than enough quality data to do machine learning. As a Data Scientist, you will develop real world experience in AI and machine learning, work with real world data, and build production worthy algorithms.

Big Data

As a Data Scientist, you will work with and gain familiarity with the latest Big Data technology stacks, including grid engine, compute clusters, hadoop, network parallel filesystems, distributed data bases and systems. We are not afraid with experimenting with the latest and newest technologies in order to get better performances.

Research Freedom

You will have tremendous leeway and freedom to define your own research direction. We are looking for scientists with tremendous creativity, out of the box thinking, and enthusiasm to test unorthodox new ideas. You will have an unparalleled opportunity to test the latest state-of-the-art machine learning and deep learning techniques.

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