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Salary
$190k – $250k per year
Location
Remote/Hybrid (New York, United States)
Seniority
Architect · 30+ years exp
Overview
Company
Impact
Profile match
PDT Partners is a quantitative asset management firm and hedge fund that applies scientific, data-driven methods to find inefficiencies in financial markets. It runs systematic trading strategies across global markets for institutional investors, built on research in statistics, machine learning, and computing. The firm originated as the Process Driven Trading group at Morgan Stanley led by Peter Muller, became independent in the early 2010s, and is based in New York City.

PDT, a quantitative investment manager, is hiring problem solvers who blend programming and applied research experience. Individuals in this role will devise, implement, evaluate, and iterate to create and improve statistical methods vetting them through direct application to enhancing our trading strategies. Applied ML scientists are expected to navigate quantitative and technical challenges within a project to advance our research methodology. They will have ample opportunity to collaborate with our deep bench of senior researchers and technologists.

PDTers are creative, energetic, friendly, entrepreneurial, and collaborative. If you could walk around our office, you’d see that we’re a focused, intent, and nimble company with none of the attitude and bureaucracy of a stereotypical Wall Street trading firm. We love to work on challenging and complicated problems, that in return give a chance to make outsized, direct impact on our bottom line. For the right talent, there is fantastic growth potential.

This is a hybrid position and will require the person to work from our New York City office at minimum3 days a week. 

Why join us?  

PDT Partners has a 30+ year track record and a reputation for excellence. Our goal is to be the best quantitative investment manager in the world-measured by the quality of our products, not their size. PDT’s very high employee-retention rate speaks for itself. Our people are intellectually extraordinary and our community is close-knit, down-to-earth, and diverse. 

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

Below is a list of skills and experiences we think are relevant. Even if you don’t think you’re a perfect match, we still encourage you to apply because we are committed to developing our people

  • Solid mathematical and analytical ability; exceptional problem-solving and modeling ability
  • Research intuition
  • Experience in programming (Python, R, Matlab, C++)
  • Excellent communication and collaborative white board skills
  • Meticulous and detail-oriented, and innately driven to understand issues deeply
  • Experience with/interested in working with large data sets
  • Self-motivated and highly-productive, with a strong sense of ownership and urgency
  • Able to work collaboratively and productively with others
  • Enjoy solving complex, difficult, real-world problems
  • Entrepreneurial and creative  
  • Finance knowledge is not required or expected
  • PhD preferred 
  • Undergraduate or Masters degree with equivalent industry experience in machine learning 
    • 2+ years of applied machine learning research experience
  • Strong publication record
  • Experience with the Python scientific stack

We take research seriously. Beyond your resume, we want to understand your research history and the papers most relevant to evaluating your candidacy. We strongly encourage you to answer all application questions and allow us to understand you as a researcher and determine where you would be the strongest fit in our team.

The salary range for this role is between $190,000 and $250,000. This range is not inclusive of any potential bonus amounts. Factors that may impact the agreed upon salary within the range for a particular candidate include years of experience, level of education obtained, skill set, and other external factors.

PRIVACY STATEMENT: For information on ways PDT may collect, use, and process your personal information, please see PDT’s privacy notices.

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