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PDT Partners

Quantitative asset management via algorithmic trading

Applied ML Scientist

Quantitative ResearchConfirmed live in the last 24 hours
Full-timeNew York, NY, USA$190k - $250k /yr

About the Job

Requirements

  • Solid mathematical and analytical ability; exceptional problem-solving and modeling ability
  • Experience in programming (Python, R, Matlab, C++)
  • Experience with Python scientific stack
  • Undergraduate or Masters degree with equivalent industry experience in machine learning
  • 2+ years of applied machine learning research experience

Responsibilities

  • Work closely with senior researchers on a variety of trading strategies and research projects, with the opportunity to conduct independent research and originate research topics over time
  • Contribute to the long-term success of our research-driven algorithmic trading business

Desired Qualifications

  • PhD preferred
  • Strong publication record
  • Experience with/interested in working with large data sets
  • Experience in programming (Python, R, Matlab, C++)
  • Experience with the Python scientific stack
  • Self-motivated and highly-productive, with a strong sense of ownership and urgency
  • Excellent communication and collaborative white board skills
  • Meticulous and detail-oriented, and innately driven to understand issues deeply
  • Entrepreneurial and creative
  • Finance knowledge is not required or expected

About the Company

PDT Partners

Quantitative asset management via algorithmic trading

PDT Partners uses quantitative models to identify market inefficiencies and predict price movements for institutional clients through algorithmic trading. It analyzes large market datasets to find patterns and executes trades with automated strategies that run with precision. Differentiators include a long history since the 1990s, emphasis on empirical validation and scientific research, and a collaborative culture that vets ideas before implementation. The goal is to deliver consistent investment returns by systematically exploiting market inefficiencies through data-driven trading.

Company size
201-500
Company stage
Not listed
Total funding
Not listed
Headquarters
New York City, New York
Founded
1993

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