Quantitative Researcher

Two Sigma, established in 2001 in New York, is a leading investment management firm that thrives at the intersection of technology, data science, and finance. The company is renowned for its innovative use of computational analysis, machine learning, and big data to predict financial market movements, making it one of the pioneers in the quantitative trading industry. Two Sigma manages assets across various markets and instruments, applying scientific methods to the investment process in order to identify unique trading and investment opportunities. The ethos of Two Sigma is deeply rooted in technology and innovation, with a strong emphasis on research and development. The firm employs a diverse team of professionals, including data scientists, engineers, and economists, who collaborate to develop sophisticated trading models and algorithms. This interdisciplinary approach enables Two Sigma to stay ahead in the fast-paced world of financial markets. Two Sigma is also committed to a culture of curiosity and continuous improvement, encouraging its employees to explore new ideas and technologies. This culture is supported by a work environment that values diversity, inclusion, and professional growth. Beyond its core business activities, Two Sigma actively engages in philanthropy and community-building initiatives, demonstrating its commitment to social responsibility. With its cutting-edge technology, innovative strategies, and a culture that fosters exploration and growth, Two Sigma continues to be at the forefront of the quantitative investment landscape.

Degree in a technical or quantitative disciplines, like statistics, mathematics, physics, electrical engineering, or computer science (all levels welcome, from bachelor’s to doctorate)
Intermediate skills in at least one programming language (like C, C++, Java, or Python)
Experience performing an in-depth research project, examining real-world data
Ability to think independently and creatively approach data analysis and communicate complex ideas clearly