New York, New York, United States
Duties: Research and implement strategies to increase quantitative trading profitability. Improve measurement, control and monitoring of portfolio risk. Apply machine learning/deep learning techniques including neural network and boosted tree methods to formulate and engineer sophisticated financial models. Develop methodologies for providing high execution quality for incoming counterparty orders and engineer systems to execute such strategies as well as monitor them. Develop low latency, high performance, robust and scalable quantitative trading software. Conceptualize and develop a sophisticated trading simulation framework. Conduct detailed, in-depth mathematical/physics-based quantitative analysis and research on market data and trading behavior. Create and handle large datasets for research and development. Apply tick-level data analysis and real-world trading experimentation to define strategy decision-making. Present quantitative research results and publish reports for internal distribution.
Minimum education required: PhD degree or equivalent in Mathematics, Physics, Computer Science, Statistics, Electrical Engineering, or related field.
Minimum experience required: 0
- Must have demonstrated knowledge of complex and technical quantitative-based mathematical and physical science (physics) skills.
- Must have demonstrated knowledge of applying mathematical and physics-based models to understand underlying mechanisms in data.
- Must have demonstrated knowledge of using a compiled high-performance programming language such as C/C++ or Java, including experience in designing and developing low latency, high performance, robust and scalable quantitative research software.
- Must have demonstrated knowledge of algorithm design, analysis and advanced (graduate level) optimization.
- Must have demonstrated knowledge of handling and analyzing complex datasets using languages such as python (with Matplotlib, Numpy, Scipy, and Pandas packages) or R. Must have demonstrated knowledge of using machine learning tools such as scikit-learn or Tensorflow.
- Must have demonstrated knowledge of Unix/Linux, Bash and Version control tools such as Git, SVN or Mercury.
- Must have demonstrated knowledge of applying numerical methods such as Monte Carlo algorithm.
- Must have demonstrated knowledge of handling large size dataset with more than 1 TB data, and caching, preprocessing and parallel computing tools to analyze large size datasets.