Quantitative Research Internship (Off-Cycle) 2026

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: We are seeking interns to work alongside experienced quantitative researchers around the world. You will be responsible for supporting quant researcher's from data study, idea generation, through to alpha construction. You will be exposed to a sophisticated quantitative research platform with access to an extensive range of large and unique datasets across the asset class spectrum. We will provide you with opportunities to refine your research skills, collaboration opportunities with other quant research teams globally, and guidance sessions on career development and your next steps. Starting in October 2026, the program will run for a duration of 6 months.

What You'll Bring

• Candidates holding or pursuing a BS, MS, or PhD in Math, Physics, Computer Science, Statistics, Engineering, or other quantitative fields with excellent academic records are strongly preferred.
• Python and shell scripting are required. Other programming languages are a plus (e.g., R, K, C#, C++, Matlab...).
• Candidates should have a scientific attitude, strong intellectual curiosity, be self-motivated, and naturally inclined to deep thinking, driven by unsolved challenges.
• Prior experience translating mathematical concepts into code is required. Any prior quantitative research experience or working in a data-intensive research environment is a plus.
• Application of machine learning, NLP, or AI techniques is a plus.
• Fluent English and excellent communication skills are required.

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