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DV Trading

Quantitative Research Intern - Summer 2027 (DV Equities)

Posted 10 Days Ago
Be an Early Applicant
In-Office
New York, NY, USA
Internship
In-Office
New York, NY, USA
Internship
Conduct quantitative research on equities market data by identifying predictive signals, building and backtesting statistical and machine learning models, and translating findings into trading strategies. The intern will work with traders and senior researchers, maintain large-scale market data pipelines, and refine research prototypes using backtest results and feedback.
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About Us:
Founded 20 years ago and headquartered in Chicago, the DV Group of financial services firms has grown to more than 600 people operating throughout North America, Europe and Asia. Since spinning out of a large brokerage firm in 2016, DV Trading has rapidly scaled as an independent proprietary trading firm utilizing its own capital, trading strategies, and risk management methodologies to provide liquidity to worldwide financial markets and hedging opportunities to commodity producers and users. Now, DV group affiliates include two broker dealers, a cryptocurrency market making firm, and a bourgeoning investment adviser.

Overview:
We are looking for a 2027 Quantitative Research Intern to join our equities team, where you will focus on generating systematic signals across multiple time horizons. This role is ideal for candidates with a strong quantitative foundation and hands-on experience in either high-frequency orderbook research or longer-term signal generation—whether through academic projects, prior internships, or independent research.
You will work side-by-side with our senior researchers and traders to explore market data, develop predictive signals, and build models that directly inform real trading decisions. This is an opportunity to gain direct exposure to how quantitative research is applied at a leading proprietary trading firm.

Responsibilities:

  • Analyze market data to uncover patterns, inefficiencies, and predictive signals across different time horizons
  • Build and backtest quantitative models using historical market data in a simulation environment
  • Apply statistical and machine learning techniques—with an emphasis on tree-based methods—to enhance signal quality
  • Collaborate closely with traders and researchers to translate research insights into robust trading strategies
  • Contribute to the development and maintenance of data pipelines for large-scale, high-frequency, and time-series market data
  • Iterate on research prototypes based on backtest results and team feedback, under the guidance of experienced mentors

Requirements:

  • Currently pursuing a Bachelor's, Master's, or PhD in a quantitative field (Mathematics, Statistics, Computer Science, Physics, Engineering, Financial Engineering, or related)
  • Expected graduation in 2027 or 2028
  • Strong proficiency in Python, including standard data science libraries (pandas, NumPy, etc.)
  • Genuine curiosity about financial markets and market microstructure
  • Solid foundation in statistics and quantitative analysis
  • Strong problem-solving skills and intellectual curiosity
  • Experience in high-frequency research and/or longer-term signal generation is a plus
  • Ability to communicate technical findings clearly to both technical and non-technical audiences
  • Self-motivated, with a strong desire to learn and collaborate in a fast-paced team environment

DV is not accepting unsolicited resumes from search firms. Only search firms with valid, written agreements with DV should submit resumes in response to DV’s posted positions. All resumes submitted by search firms to DV via e-mail, the Internet, personal delivery, facsimile, or any other method without a valid written agreement shall be deemed the sole property of DV, and no fee will be paid in the event the candidate is hired by DV. DV is proud to be an equal opportunity employer and committed to creating an inclusive environment for all employees.

DV Trading New York, New York, USA Office

New York, United States

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