PhD Quant Research Intern — Real Markets, Real Impact

Trading Interview

New York (NY)

Sur place

USD 270 000 - 330 000

Plein temps

14 jours+

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Résumé du poste

Jump Trading Group offers a 10-week PhD quant research internship in Chicago and New York, open to current PhD students. The program covers research process, ML, statistics, trading, Python, and the infrastructure used all summer; you'll be matched with a trading team based on your background and interests, working 1:1 with experienced researchers on a live project tied to business needs.

You'll explore a broad spectrum of methods, from classical statistics to deep learning, and develop

Qualifications

  • Currently pursuing a PhD in a quantitative field with emphasis on research.
  • Strong data analysis, statistics, and programming skills required.
  • Ability to form hypotheses, design tests, and interpret results.
  • Experience with real-world data and research projects is preferred.
  • Excellent communication and collaborative work style.

Responsabilités

  • Match with a trading team and own a research project end to end.
  • Collect, clean, and explore large datasets to generate predictive signals.
  • Build, fit, and evaluate models on a computing grid and present results.
  • Receive daily 1:1 mentorship and increase autonomy over the summer.
  • Contribute to live business needs with rigorous research.

Connaissances

PhD candidate
Python programming
Statistical modeling
Data analysis
Team collaboration

Formation

PhD in Statistics/Mathematics/CS/Physics

Outils

Python
C++

Description du poste

Jump Trading Group offers a 10-week PhD quant research internship in Chicago and New York, open to current PhD students. The program covers research process, ML, statistics, trading, Python, and the infrastructure used all summer; you'll be matched with a trading team based on your background and interests, working 1:1 with experienced researchers on a live project tied to business needs.

You'll explore a broad spectrum of methods, from classical statistics to deep learning, and develop

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