Aqr Capital Management LLC

Aqr Capital Management LLC

Quantitative Research Associate (AQR Capital Management, LLC, Greenwich, CT)

Greenwich, CT

Sponsorship not specified$240k-$260kDetected 27 days ago
AlgorithmsMachine LearningData AnalysisStatisticsForecastingResearchCollaboration

About the role

  • AQR is a global investment firm built at the intersection of financial theory and practical application. We strive to deliver concrete, long-term results by looking past market noise to identify and isolate the factors that matter most, and by developing ideas that stand up to rigorous testing. By putting theory into practice, we have become a leader in
  • alternative strategies and an innovator in traditional portfolio management since 1998.

Responsibilities

  • Add features to proprietary research system to implement new research ideas.
  • Participate in the design and development of research infrastructure for the purpose of conducting economic and statistical research.
  • Develop and maintain analytical tooling and infrastructure.

Nice to have

  • Engage in the development of proprietary quantitative investment strategies.
  • Participate in a variety of integral business functions, including research, data analysis, portfolio optimization, and risk management.
  • Work closely with portfolio managers to assist in the implementation of investment strategies.
  • Telecommuting permitted two days per week.
  • extracting features from textual data, automating research tasks, and supporting dataset construction
  • conducting systematic signals research employing time-series and cross-sectional methods, rigorous backtesting, and robust model-validation techniques
  • translate research models into implementable portfolio-construction and trading workflows
  • monitoring strategy behavior, exposures, and alignment with research expectations

Compensation

  • The salary range is $240,000.00 - $260,000.00/year.

This listing is sourced directly from Aqr Capital Management LLC's careers page and normalized into a canonical job model.