Quantitative Research Intern (Available to start in Nov 2024)

Job Description

WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform.

WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement.

Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it.

The Role: 

 

Research is at the core of WorldQuant. Through rigorous exploration and unconstrained thinking about how to apply data to the financial markets, our researchers are in constant search of new alphas. We strive to understand data in ways our competitors don’t believe is possible. Researchers at WorldQuant employ tested processes seeking to identify high-quality predictive signals that we believe are undiscovered by the wider market. These signals are mathematical expressions of data that are used as inputs in our quantitative models.
WorldQuant is seeking an exceptional individual to join the firm as a Quantitative Research Intern. The person must have a strong understanding of the investment research process to create computer-based models that seek to predict movements of global financial markets. While prior finance experience is not required, a successful candidate must possess a strong interest in learning about finance and global markets. Candidates will have a research scientist mind-set; be a self-starter, a creative and persevering deep problem solver who is motivated by unsolved challenges.


This position is responsible for assisting with daily research and analysis tasks – which includes scripting for monitoring, portfolio visualization and alpha signal analysis. Candidates need not have prior knowledge of financial markets, but must have a strong interest in learning about stock markets and other capital markets. Our highly accomplished senior staff will provide the interns with mentoring and guidance to help them succeed.

 

What You’ll Bring:

  • Candidates holding or pursuing a BS (Hons), MS or PhD in in Math, Physics, Computer Science or Engineering with excellent academic records are strongly preferred.
  • Exceptional candidates without an advanced degree will also be considered. Prior quant analysis or trading experience is a benefit.
  • Have experience working under a Linux environment
  • Have experience with scripting language, Python and shell scripting are required
  • Programming skills, python is a must
  • Machine learning and / or linear algebra course work is a plus
  • Participated in data mining and or programming competitions (preferred)
  • Currently at final/penultimate year of studies
  • Final year students (graduating 2025) who are available to commit full-time internship for at least 3 months, to start either January or May 2025 (summer). Graduate placements are possible, subject to internship performance.
  • ‚ÄãWorldQuant has adopted the Tripartite Standard on Recruitment Practices, and has committed to be a fair and progressive employer.

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WorldQuant is an equal opportunity employer and does not discriminate in hiring on the basis of race, color, creed, religion, sex, sexual orientation or preference, age, marital status, citizenship, national origin, disability, military status, genetic predisposition or carrier status, or any other protected characteristic as established by applicable law.

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