Primary Responsibilities:
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Development and support of quantitative stock selection models.
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Systematic discovery of alpha signals using traditional and alternative datasets.
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Incorporate machine learning techniques into systematic strategy research and development.
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Help ensure that quantitative models, research tools and risk controls are adequate to support new product initiatives.
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Collaborate with other team members and other groups in order to drive productivity.
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Support technological infrastructure and analytical research function.
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Performs related duties as assigned.
Preferred Qualifications:
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Minimum of five years’ experience in quantitative research and quantitative model development in any asset classes.
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A graduate degree in a quantitative discipline (e.g., Financial Engineering, Econometrics, Computer Science, Engineering, Math, Physics) is required.
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High-level proficiency in Python, R or similar programming languages.
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Previous hands-on experience in quantitative research, data science and predictive Modelling including machine learning is a huge plus.
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Demonstrated ability to learn and work with cutting-edge statistical methods and computational techniques.
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In-depth working knowledge of quantitative systems development, tools and engineering techniques including back testing, optimization, attribution, risk control and quality control.
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Knowledge of industry standard tools and databases such as Factset, Bloomberg, Worldscope, Compustat, IBES, Barra, etc.
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Ability to take ownership of projects, with a strong focus on quality and accuracy.
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A passion for investments combined with a strong work ethic, integrity, and a commitment to individual development.