Quantitative Trading Internship — Mentored, Hands-On Alphas

Trading Interview

Pennsylvania, United States, Northern America, Americas

Hybrid

USD 25.000 - 36.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

Schaffe es an den ATS-Filtern vorbei

Benefits dieser Stelle

Mentorship program
Education program access

Zusammenfassung

Susquehanna is offering a Quantitative Systematic Trading Internship where you will work on projects that model the work of full-time employees, learn through a formal education program, and receive mentorship from top professionals. You will have opportunities to build alphas on actual trading strategies and collaborate with multiple teams across the firm.

Ideal candidates are PhD students in quantitative fields, strong problem-solvers, and proficient in Python with knowledge of C++ being a

Qualifikationen

  • PhDs in quantitative fields (penultimate or final year)
  • Analytical problem-solvers with strong data-to-decision skills
  • Clear communicators in a fast-paced, collaborative environment
  • Programmers comfortable with large data sets in Python; C++ a plus

Aufgaben

  • Model market behavior and generate alphas using probabilistic and ML methods
  • Backtest ideas using historical data and refine strategies
  • Collaborate across teams and explore breadth of quant work
  • Participate in a comprehensive education program with mentorship

Kenntnisse

Analytical thinking
Clear communication
Self-motivated
Quick learner
Collaborative mindset

Ausbildung

PhD in Mathematics/Physics/Statistics/EE/CS/OR/Economics

Tools

Python
C++

Jobbeschreibung

Susquehanna is offering a Quantitative Systematic Trading Internship where you will work on projects that model the work of full-time employees, learn through a formal education program, and receive mentorship from top professionals. You will have opportunities to build alphas on actual trading strategies and collaborate with multiple teams across the firm.

Ideal candidates are PhD students in quantitative fields, strong problem-solvers, and proficient in Python with knowledge of C++ being a

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