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Trexquant

Quantitative trading via ML models

Quantitative Researcher - Quantitative Research and Machine Learning

Quantitative ResearchConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A PhD in Computer Science, Physics, Mathematics, Statistics, Engineering, Economics, or another STEM discipline is required.
  • A strong interest in machine learning, statistics, or artificial intelligence is required.
  • Strong programming skills in Python are required.
  • Outstanding analytical and problem-solving abilities are required.
  • The ability to work independently while collaborating effectively with multidisciplinary teams is required.
  • Curiosity, creativity, and a passion for solving difficult problems are required.

Responsibilities

  • Design and implement state-of-the-art machine learning models for large-scale prediction problems.
  • Work with massive structured and alternative datasets to uncover meaningful signals.
  • Develop novel statistical methods and predictive features.
  • Read, evaluate, and extend cutting-edge academic research.
  • Design experiments, test hypotheses, and validate ideas through simulation.
  • Collaborate with experienced researchers across disciplines to transform research into production-ready strategies.
  • Conduct research that has a direct impact from idea generation to live trading.

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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