Quantitative Researcher

About Cubist

Cubist Systematic Strategies, part of the Point72 family, focuses on systematic and computer-driven trading approaches across a variety of highly liquid asset markets, such as equities, futures, and foreign exchange. Their strategy emphasizes deep research into various market irregularities, leveraging access to a broad spectrum of public data sources.

Job Description

In this role, researchers independently perform quantitative finance investigations, concentrating on the creation and application of statistical and predictive modeling techniques. Candidates oversee the entire research lifecycle, including choosing methodologies, gathering and analyzing data, conducting tests, developing prototypes, backtesting models, and continuously assessing performance.

Team members come from diverse backgrounds, including similar roles at other financial firms, adjacent sectors, or directly from academic institutions. These individuals excel through a combination of strong technical proficiency, a drive for solving complex problems, and a keen interest in financial market dynamics and human behavioral patterns. Guidance and support are provided by an experienced group of portfolio managers and researchers.

Candidate Profile

• Possession of a Master’s degree or PhD in disciplines such as finance, computer science, mathematics, physics, or other quantitative fields
• Between three to seven years of experience in alpha-oriented quantitative research relating to equities, futures, fixed income, credit, or foreign exchange markets
• Excellent quantitative and analytical capabilities
• Proven track record performing independent research with sizable datasets
• Proficiency in programming languages such as C++, Java, C#, MATLAB, R, Python, or Perl
• Strong attention to detail
• Capability to take full responsibility for assigned projects and to work autonomously or collaboratively within a smaller team setting

Additional Information

The organization values candidates with extraordinary enthusiasm and invites applicants to share notable personal accomplishments beyond finance. They encourage highlighting special engineering or data-centric projects completed independently or during academic studies. The team also includes accomplished statisticians, engineers, musicians, writers, mathematicians, and non-profit founders, reflecting a diverse and passionate work culture.