Quant Research Internship: Mentored Alpha Modeling & Growth

Trading Interview

New York (NY)

On-site

USD 348,000 - 434,000

Full time

14 days+

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Benefits offered by this job

Signing bonus
Housing
Meals (breakfast & lunch)

Job summary

Susquehanna offers a Quantitative Research Internship designed to model the work of our full-time staff and provide a structured education program with mentors who are leaders in the field. Interns will have opportunities to build alphas on an actual trading strategy in a collaborative environment.

PhD candidates in quantitative fields will work across modeling, execution, and evaluation, using Python and possibly C++.

Qualifications

  • PhDs in penultimate or final year in quantitative fields such as mathematics, physics, statistics, electrical engineering, computer science, operations research, or economics.
  • Analytical problem-solvers with ability to turn data into decisions.
  • Clear communicators in a fast-paced, collaborative environment.
  • Programmers comfortable processing and analyzing large data sets in Python; C++ is a plus.

Responsibilities

  • Modeling using probability, statistics and ML to predict market behavior and generate alphas.
  • Develop strategies to execute ideas in simulated environments.
  • Backtest ideas using historical data and refine strategies.
  • Explore all aspects of quant work and Susquehanna's business areas.
  • Participate in a comprehensive education program with mentorship.
  • Collaborate across teams and gain exposure to different groups.

Skills

Python
C++
Data analysis
Machine learning

Education

PhD in quantitative field

Job description

Susquehanna offers a Quantitative Research Internship designed to model the work of our full-time staff and provide a structured education program with mentors who are leaders in the field. Interns will have opportunities to build alphas on an actual trading strategy in a collaborative environment.

PhD candidates in quantitative fields will work across modeling, execution, and evaluation, using Python and possibly C++.

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