Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.
KEPL is a start-up trading team at Cubist Systematic Strategies. We are an elite team specialized in trading medium-frequency statistical arbitrage strategies with high Sharpe. The team is led by an ex-research head of D.E. Shaw and other founding members consist of industry veterans from top tier trading and tech firms, including Two Sigma, Citadel, Tower Research, Facebook AI Lab, etc. We have a collaborative culture, and we value rigorous research and innovative technologies.
We are looking for exceptional students to be our quantitative researcher interns for the summer of 2023. An ideal candidate should have a strong passion and initiative to work in a start-up team environment. He/she should have strong analytical skills and be able to solve hard problems rigorously. Our typical intern candidates come from quantitative programs of top US universities.
During the internship, the interns will collaborate closely with our full-time researchers and work on brand new quant trading models with real production impact. After the internship, we will provide full-time offers for interns with good performance.
PhD candidate in math, physics, statistics, computer science, engineering or other quantitative fields
Strong knowledge of computational math, probability, and statistics
Strong analytical skills, with attention to details
Willing to work in a fast-paced start-up environment
Willing to learn and take ownership
Strong programming skills in Python, or C/C++
Good communication skills
The annual base salary is $150,000-$200,000 (USD) which will be prorated based on internship start and end date. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.