New York, New York, United States
Duties: Responsible for researching, designing and developing predictive quantitative financial modeling systems by using advanced machine learning and statistical analysis skills in the application of mathematical and statistical principles. Applying quantitative data mining, pattern recognition and statistical analysis techniques including regression, dimensionality reduction and time-series methods to formulate and engineer sophisticated financial modeling systems. Developing production-quality, high reliability and highly-tuned numerical code. Use knowledge of probability and statistical learning to perform predictive analysis. Use advanced and thorough understanding of complex and mathematical/statistical and engineering skills to aid in the use of modeling techniques, numerical methods, and keep abreast of the latest research findings in finance and machine learning academic journals. Utilize knowledge of mathematical and statistical methods and instruments including quantitative alpha and behavioral research, probability analysis, statistical inference and numerical optimization. Present complex machine learning research techniques in a format accessible to scientists, not necessarily experts in the field.
Minimum education and experience required: PhD degree or equivalent in Computer Science, Electrical Engineering, Statistics, Mathematics, or related field OR Master’s degree or equivalent in Computer Science, Electrical Engineering, Statistics, Mathematics, or related field plus 3 years of quantitative research experience, or related experience.
Must have demonstrated knowledge of statistics and machine learning techniques including regression analysis, principal component analysis, dimensionality reduction and time series analysis for building predictive models.
Must have demonstrated knowledge performing advanced statistical data analysis and predictive machine learning modeling on large, sparse datasets.
Must have demonstrated knowledge of interpreting predictions of complex theoretical models and formulating concrete hypotheses based on them, which can subsequently be tested with the help of numerical simulations.
Must have demonstrated ability to develop production-quality high reliability and highly-tuned numerical computer program code in Python and R.
Must have demonstrated knowledge in conducting academic research in a highly quantitative field of science.
Must have demonstrated ability to understand and implement the latest research findings in finance and machine learning journals.
Must have theoretical and practical experience with algorithms for sparse high dimensional data.
Must have experience presenting complex machine learning research techniques in a format accessible to scientists, not necessarily experts in the field.
Must also pass company’s required skills assessment.
Employer will accept any amount of graduate coursework, graduate research experience or professional experience with the required skills.