Quantitative Researcher - Machine Learning

ABOUT CUBIST

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.

JOB DESCRIPTION

Researchers are responsible for independently conducting quantitative finance research with a focus on statistical and predictive models. Successful researchers manage all aspects of the research process including methodology selection, data collection and analysis, testing, prototyping, backtesting, and performance monitoring.

Some successful researchers have joined us from similar backgrounds at other firms. Others have joined from related fields or directly from academia and have thrived with hands on guidance from our large team of experienced portfolio managers and researchers. Our most exceptional team members combine strong technical skills and a passion for problem solving with an intense curiosity about financial markets and human behavior.

DESIRABLE CANDIDATES

• MS or PhD candidates in finance, computer science, mathematics, physics, or other quantitative discipline
• 3-7 years of experience in alpha driven quantitative research for equities, futures, fixed income, credit, and/or FX
• Strong analytical and quantitative skills
• Demonstrated ability to conduct independent research utilizing large data sets
• Programming in any of the following: C++, Java, C#, MATLAB, R, Python, or Perl
• Detail-oriented
• Willing to take ownership of his/her work, working both independently and within a small team

We’re looking for exceptional colleagues with unparalleled passion. If you’d like your resume to stand out, tell us about your exceptional personal achievements, even if they have nothing to do with finance. Of course we love to hear more about specific engineering or data projects that you’ve worked outside of school, or as part of your curriculum. If you’re proud of the work you did we want to hear about it. In addition to exceptional statisticians and engineers, we work with talented musicians, writers, mathematicians, and founders of non-profits; we’d love to learn more about what excites you.