Trexquant

Trexquant

Quantitative Researcher - Experienced Hires (USA)

Stamford, Connecticut, United States

Sponsorship not specified$130k-$200kDetected 327 days ago
PythonAlgorithmsMachine LearningResearchProblem Solving

About the role

  • Trexquant is a systematic hedge fund where we use thousands of statistical algorithms to trade equity, futures and other markets globally.
  • Continuously innovate and improve existing models by integrating new data sources and advanced techniques to boost performance and scalability.
  • The base salary for this role is $130,000 to $200,000, and will be determined based on the candidate's educational background and professional experience.

Responsibilities

  • Parse and analyze large datasets to identify actionable alpha signals and develop strategies for systematic trading.
  • Investigate and implement state-of-the-art academic research in the field of quantitative finance.
  • Collaborate closely with a team of experienced quantitative researchers to conduct experiments, backtest hypotheses, and refine strategies through rigorous simulations and data analysis.

Compensation

  • The base salary for this role is $130,000 to $200,000, and will be determined based on the candidate's educational background and professional experience.

Benefits

  • Starting with many data sets, we develop large sets of features and use various machine learning methods to discover trading signals and effectively combine them into market-neutral portfolios.
  • Design, implement, and optimize various machine learning models aimed at predicting liquid assets using a wide set of financial data and a vast library of trading signals.
  • Base salary is one component of Trexquant's total compensation package, which may also include a discretionary, performance-based bonus.

Company info

  • While we are open to researchers in any asset class we are currently focusing on roles in equities, futures, commodities, and event driven research.

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