Machine Learning Researcher – New York, NY

Machine Learning Researcher

 

Our client is one of the world’s premier investment firms. The firm deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of their effort is rigorous research into a wide range of market anomalies, fueled by their unparalleled access to a wide range of publicly available data sources.

Job Description

Researchers are responsible for applying, adapting, and extending existing results in the broad field of machine learning, while also conducting Client research as required. They are interested in all aspects of Machine Learning including: predictive modelling, clustering, time series analysis, natural language processing, and computer vision. Successful researchers manage all aspects of the research process including methodology selection, data collection and analysis, implementation and testing, prototyping, and performance evaluation.
Some successful researchers have joined the from similar backgrounds at other firms. Others have joined from related fields or directly from academia and have thrived with hands on guidance from their large team of experienced portfolio managers and researchers. Their most exceptional team members combine strong technical skills and a passion for problem solving with an intense curiosity about financial markets and human behavior.

Desirable Candidates

  • PhD or PhD candidate in machine learning, computer science, statistics, or a related field.
  • Superb analytical and quantitative skills, along with a healthy streak of creativity.
  • Demonstrated ability to conduct independent research utilizing large data sets.
  • Passion for seeing research through from initial conception to eventual application.
  • Curiosity about financial markets.
  • Strong scientific programming in Python, R or Matlab.
  • Empirical, detail-oriented mindset.
  • Sense of ownership of his/her work, working well both independently and within a small collaborative team.

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