Quantitative Researcher
PDT Partners
Quantitative Researcher
PDT Partners
Quantitative asset management via algorithmic trading
Experience Required: Entry-level (PhD Program) or Experienced (Postdoc, Faculty, Scientific Lab)
Education: PhD in Math, Science, Engineering and other relevant disciplines
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.
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.
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.
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