Quantitative Researcher (Mid-Freq)

Requirements

• 3+ years of prior work experience in stat-arb required
• Degree in a quantitative or technical discipline (e.g. statistics, computer science, physics, mathematics, economics)
• Exceptional academic credentials
• Demonstrated ability to conduct research using large noisy real-world datasets
• Exceptional attention to detail and desire to understand issues deeply
• Outstanding work ethic and ability to thrive in a fast-paced environment
• Strong numerical programming skills, including proficiency in Python for data analysis and machine learning. Experience with C++ a plus
• The estimated base salary range for this position is $175,000 to $300,000 per year, based on job-related skills and experience. This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package.
• Culture
• Hudson River Trading (HRT) brings a scientific approach to trading financial products. We have built one of the world's most sophisticated computing environments for research and development. Our researchers are at the forefront of innovation in the world of algorithmic trading.

Benefits

Additional Information

HRT is seeking quantitative researchers to join our effort in developing mid-frequency systematic trading strategies. Candidates will apply rigorous statistical methods on a wide range of datasets and implement trading models based on novel predictions of market behavior, all while leveraging HRT's world-class research and trading infrastructure. Successful candidates will be part of a growing effort and have the opportunity to contribute to all aspects of strategy development, including alpha generation, portfolio construction/optimization and trade execution algorithms. Researchers are responsible for not only prototyping and conducting research into various strategy components, but also writing code to productionalize their ideas; thus, interest and experience in programming are essential. HRT employees enjoy a collegial and non-siloed environment; candidates will work closely with other researchers to develop new ideas and refine existing trading models.

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