Overview
This is an opportunity for students and researchers of advanced data modeling and statistical learning methods to apply these techniques to market prediction and systematic trading. Job Responsibilities Pre-process (validate, clean, normalize, reduce dimension) very large data sets for model estimation and event studies Identify features and relationships useful for the predictive modeling of market dynamics Desirable Candidates Undergraduate, MS, or PhD candidates in finance, computer science, mathematics, physics, or other quantitative discipline Programming in any of the following: C++, Java, C#, MATLAB, R, Python, or Perl Strong analytical and quantitative skills Demonstrated interest in financial markets and systematic trading Clear, concise, and proactive communicator Detail-oriented Willing to take ownership of his/her work, working both independently and within a small team The a
Complete internship details
Full Job Description
This is an opportunity for students and researchers of advanced data modeling and statistical learning methods to apply these techniques to market prediction and systematic trading.
Job Responsibilities
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Pre-process (validate, clean, normalize, reduce dimension) very large data sets for model estimation and event studies
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Identify features and relationships useful for the predictive modeling of market dynamics
Desirable Candidates
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Undergraduate, MS, or PhD candidates in finance, computer science, mathematics, physics, or other quantitative discipline
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Programming in any of the following: C++, Java, C#, MATLAB, R, Python, or Perl
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Strong analytical and quantitative skills
-
Demonstrated interest in financial markets and systematic trading
-
Clear, concise, and proactive communicator
-
Detail-oriented
-
Willing to take ownership of his/her work, working both independently and within a small team
The annual base salary is $120,000-$180,000 (USD) which will be prorated based on internship start and end date. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
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