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Hedge Funds
Quantitative Researcher - Intern
at Point72
InternshipNo visa sponsorshipData Science/AI/ML
Posted 20 hours ago
No clicks
- Compensation
- Not specified
- City
- London, Hong Kong, Tokyo, Paris
- Country
- United Kingdom, China, Japan, France
Currency: Not specified
Opportunity for students and researchers to apply advanced data modeling and statistical learning techniques to market prediction and systematic trading. Responsibilities include preprocessing very large datasets (validation, cleaning, normalization, dimensionality reduction) and identifying features and relationships useful for predictive modeling of market dynamics. Candidates should be quantitative (Undergraduate/MS/PhD) in fields such as finance, computer science, mathematics or physics and proficient in programming (C++, Java, C#, MATLAB, R, Python, or Perl). The role requires strong analytical skills, attention to detail, clear communication, and the ability to work independently and in small teams.
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Quantitative Researcher - Intern Career
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Quantitative Researcher - Intern
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Experience
Internships
Location
London | Hong Kong | Tokyo | Paris
Focus
Systematic Investing
Business
Cubist
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
- 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
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Job Description
\nThis 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
\n- 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
\n- 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
\n
\n
\n
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Quantitative Researcher - Intern
at Point72
Back to all Data Science / AI / ML jobs
Hedge Funds
Quantitative Researcher - Intern
at Point72
InternshipNo visa sponsorshipData Science/AI/ML
Posted 20 hours ago
No clicks
- Compensation
- Not specified
- City
- London, Hong Kong, Tokyo, Paris
- Country
- United Kingdom, China, Japan, France
Currency: Not specified
Opportunity for students and researchers to apply advanced data modeling and statistical learning techniques to market prediction and systematic trading. Responsibilities include preprocessing very large datasets (validation, cleaning, normalization, dimensionality reduction) and identifying features and relationships useful for predictive modeling of market dynamics. Candidates should be quantitative (Undergraduate/MS/PhD) in fields such as finance, computer science, mathematics or physics and proficient in programming (C++, Java, C#, MATLAB, R, Python, or Perl). The role requires strong analytical skills, attention to detail, clear communication, and the ability to work independently and in small teams.
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P72 Careers - Header
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Quantitative Researcher - Intern
Apply Now
Experience
Internships
Location
London | Hong Kong | Tokyo | Paris
Focus
Systematic Investing
Business
Cubist
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
- 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
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Job Description
\nThis 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
\n- 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
\n- 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
\n
\n
\n
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