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Squarepoint Capital

Manages quant-driven, systematic portfolios globally

Trading Applications Specialist

Quantitative Trading / Algorithm DevelopmentConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • Degree in Computer Science, Mechanical Engineering, Mathematics or a similar discipline
  • Up to 4 years experience in a similar environment, buy or sell side
  • Experience with Linux, Python, Bash
  • Knowledge of at least one of the following asset classes: equities, fixed income, derivatives, etc.
  • Exceptional communication skills, attention to detail and ability to work within a global team
  • Eagerness to continuously learn new technologies and develop with us
  • Curious and driven by the challenge of solving new problems

Responsibilities

  • Carry out ready for business checks ahead of the start-of-day to ensure a smooth market open, resolving any failures
  • Monitor trading applications and execution strategies, utilizing our proprietary observability frameworks
  • Carry out post trade checks and resolve reporting issues
  • Leverage your critical thinking skills and manage outages that impact the trading environment, reducing production downtime
  • Work on a wide range of projects, including onboarding of new applications, services and products into the production environment or developing tools and scripts to automate operational tasks
  • Take ownership of incident management and root cause elimination through process improvements

Desired Qualifications

  • Experience with either of: kdb+/q, MSSQL, PostgreSQL
  • Experience working with the FIX protocol
  • Understanding of TCP/IP concepts

About the Company

Squarepoint Capital

Manages quant-driven, systematic portfolios globally

Squarepoint Capital is a global investment manager that uses quantitative and systematic strategies to build diversified portfolios across asset classes, trading frequencies, and global markets for institutional investors. Its approach relies on a large data platform and advanced technology to power research and model-based trading decisions, with portfolios continually adjusted in response to market data. The firm differentiates itself through a broad, multi-asset, multi-frequency framework, a global footprint with offices in thirteen cities, and a collaborative culture that emphasizes rigorous research and disciplined execution. Its goal is to deliver attractive, risk-aware returns for pension funds, endowments, and sovereign wealth funds through systematic, data-driven investment processes.

Company size
501-1,000
Company stage
Not listed
Total funding
$1.4B
Headquarters
London, United Kingdom
Founded
2014

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