Internship - Quantitative Trading

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.

Role

Entry-Level Quantitative Researchers are responsible for conducting rigorous quantitative research with a focus on predictive models. You will be trained in all aspects of systematic trading from idea generation all the way to practical trading considerations. Successful hires will ultimately become thought leaders within our collaborative research group.

Responsibilities

• Conduct original quantitative alpha signal research
• Follow, digest and analyze the latest academic research
• Manage all aspects of the research process, including idea generation, data analysis, hypothesis development and testing, alpha discovery, trading strategy generation, backtesting and portfolio analysis
• Build analytical tools to supplement our shared research framework 

Requirements

• B.S., M.S. or PhD in finance, economics, mathematics, statistics, data science, computer science, or other quantitative discipline.
• Programming in Python (or comparable language) and working knowledge of SQL
• Strong analytical and quantitative skills.
• Willingness to take ownership of his/her work.
• Ability to work both independently and collaboratively within a team.
• Strong desire to deliver high quality results in a timely fashion.
• Detail-oriented.
• Prior experience in the financial services industry is not required. 
• A commitment to the highest ethical standards. 

The annual base salary range for this role is $150,000-$200,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. 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.


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