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Trexquant

Quantitative trading via ML models

Trading Systems Operations Engineer

Quantitative DevelopmentConfirmed live in the last 24 hours
Full-timeStamford, CT, USAHybrid

About the Job

Requirements

  • A Bachelor of Science or Master of Science in Computer Science, Information Technology, or a STEM-related field is required.
  • At least 3 years of experience as a systems operations engineer is required; experience in financial services is preferred.
  • Strong quantitative analytical skills are required.
  • Proficiency in Linux systems is required.
  • Experience with a scripting language such as Python, Perl, or Bash is required.
  • The candidate must be able to interface with internal and external clients.
  • The candidate must be able to work independently in a fast-paced, high-pressure environment.
  • Understanding of market data and order entry systems, and an interest in modern electronic markets, are required.

Responsibilities

  • Monitor trading systems and infrastructure components to ensure optimal performance and reliability.
  • Support implementation of new trade functionality, such as new asset classes or new prime brokers, including establishing FIX sessions, file encryption, and FTP setup.
  • Proactively identify and resolve potential issues to minimize downtime and optimize system efficiency.
  • Respond to system alerts and incidents, investigate root causes, and implement solutions to prevent recurrence.
  • Provide technical and analytical support to Research, Accounting, and Compliance teams, including troubleshooting and ad hoc analysis of production data.

Desired Qualifications

  • Use automation tools, including AI-based solutions, to improve accounting or operational workflows.

About the Company

Trexquant

Quantitative trading via ML models

Trexquant is a quantitative finance firm that builds multi-asset portfolios using machine learning. It develops investment and research tools that ingest a wide range of data variables to create complex trading models and strategies, which in turn generate trading signals. These signals guide decisions across global markets. The product works by applying advanced machine learning to large datasets to create predictive models and rule-based strategies, which drive portfolio construction and trading actions through the firm’s platform. Trexquant differentiates itself by emphasizing the breadth of data it uses and the depth of machine-learning driven models to craft multi-asset portfolios, aiming to produce signals that outperform global market conditions. The company’s goal is to generate investment performance that beats benchmarks by using data-driven signals and diversified, multi-asset strategies.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
Stamford, CT
Founded
2012

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