About: Selby Jennings has partnered with the Quantitative Strategies team of an elite, multi-strategy hedge fund. This is a team of individuals coming from various backgrounds between big tech and the quant space. They are looking for talented developers who can work rapidly on implementing and supporting technical processes and infrastructure for automated trading strategies in both equity statistical arbitrage and volatility arbitrage.
Qualifications:
Bachelors (Master's or PhD preferred) in Computer Science, Math, Physics, or related field
A minimum of 2 years of professional experience in Python, including package development
Experience in UNIX/Linux/BSD environments
Experience with relational database management system (PostgreSQL, MySQL, etc)
Proficiency in one object-oriented or lower-level language (Java/C++/C)
Responsibilities:
Work directly as member of the Quantitative Development team, collaborating daily with other Quantitative Developers, Researchers, and Portfolio Managers.
Develop quantitative infrastructure for alpha generation, portfolio construction, and algorithmic trading.
Provide software contributions to evolve the Quantitative strategies platform and support daily alpha construction and trading, including:
Develop the proprietary trading system, data warehouse, service-based architecture, machine learning platforms, and simulation tooling.
Work directly with traders and researchers to build new proprietary trading strategies and alphas.
Provide high-level technical and investment support to the team.
Parse and analyze new data sources such as exchange data, company fundamental data, or unstructured data.
Design reporting and analysis tools for strategy risk, trade cost and execution using data from a proprietary columnar database.
Own the end-to-end design, implementation, and launch of software projects spanning the technical domains described above.
Value Added:
Experience with Git
Experience with KDB/Q
Familiarity with popular machine learning/deep learning/statistical packages (such as scikit-learn, TensorFlow, PyTorch, etc.)