Location: New York, New York, United States
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Duties: Research, develop, and back‐test quantitative execution planning models by using statistical analysis and machine learning methods. Keep up with literature in quantitative finance, asset pricing, and machine learning by reading journal articles and attending academic talks. Use various programming languages and statistical software to clean, organize, manipulate raw data sets for the purpose of further analysis. Apply data mining, pattern recognition, and statistical analysis methods, including linear and non-linear forecasting techniques and time series methods to research, design, and develop sophisticated quantitative statistics-based financial modeling systems. Develop production‐quality, high-reliability, highly tuned mathematics‐based numerical code and use knowledge of matrix algebra, mathematical probability, and statistical learning to perform quantitative predictive research and analysis and risk evaluations.
Minimum education required: Doctoral Degree (PhD)* in Mathematics, Statistics, Finance, Physics, or Economics. *Note: Will accept completion of degree requirements for Doctoral Degree (PhD), if candidate has not yet received/been conferred the actual degree, as satisfaction of this requirement.
Skills required: Must have knowledge of the following:
Statistical models for analyzing large-scale data sets and ability to translate academic findings into actionable strategies;
Statistical theory and methods such as parametric and non-parametric inference and Bayesian methods and ability to apply them in real-world context while knowing their respective limitations;
Machine learning techniques such as Bayesian methods, ensemble learning, and neural networks, their applications in the quantitative finance space, and ability to use these methods in constructing the model pipeline;
Obtaining, cleaning, and transforming data and ability to conduct exploratory analysis using visualization tools such as ggplot, matplotlib, and Tableau;
Data analysis, especially dealing with non-ideal practical data where missing data, extreme outliers need careful handling using R, Python, and Matlab software;
Unix operation system and ability to write production-level Java, Python, and Bash script code; and
Optimizing, accelerating, and parallelizing high-intensity computations.
Must also pass company’s required skills assessment.