Point72 Lp

Point72 Lp

Data Engineer

New York

Sponsorship not specifiedDetected 9 days ago
PythonFull-Stack DevelopmentSQLLinuxPandasData AnalysisData EngineeringData ScienceResearchCommunication

About the role

  • About Cubist: Cubist Systematic Strategies is one of the world's premier investment firms.
  • The firm 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.

Responsibilities

  • Improve data ETL pipeline and build tools to analyze new data efficiently.
  • Build technologies to bolster research & trading efficiency.
  • Manage day-to-day operations in a fast-paced environment.
  • In this team, the candidate will gain full-stack exposure and build expertise in multiple aspects of quantitative trading.

Requirements

  • Master/PhD degree in math, computer science, engineering, or other related fields.
  • 1-3 years of professional experience in software development or data science/analytics.
  • Familiarity with the Linux environment.

Nice to have

  • Proficiency in Python
  • knowledge of common data analytics tools (e.g., SQL, pandas) is a plus.

Company info

  • KEPL is a fast-growing team at Cubist Systematic Strategies.
  • We are specialized in medium-frequency statistical arbitrage strategies with high Sharpe.
  • The team is made up of people from top universities and top tier trading and tech firms, including: D.E. Shaw, Two Sigma, Citadel, Meta, Google, etc.
  • We have an open and collaborative culture, and we value rigorous research and innovative technologies.
  • Please send CVs to kepl-talent@cubistsystematic.com with "2025 KEPL DE Application" in the subject line.
  • We are looking for a quantitative software developer to join our team and contribute to multiple initiatives that aim to expand our business.

This listing is sourced directly from Point72 Lp's careers page and normalized into a canonical job model.