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Point72

Cubist Quantitative Researcher

Singapore

Location
Singapore
Work
Not stated
Employment
Full-time
Level
Not stated
Function
Data & AI
Pay
Not stated
Posted
27 months ago

About the role

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

REQUIRMENTS

  • 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.

Source

Posted by Point72 on their own hiring page, boards.greenhouse.io. OptiTalent shows it as published, read 07 Nov 2026 00:48 UTC; the original is the reference.

View the original ad ↗

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