Quantitative Researcher - Experienced (DV Equities)

Founded 20 years ago and headquartered in Chicago, the DV 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,

Overview

We are looking for an experienced Quantitative Researcher to join our equities team. This role is open to candidates with hands-on experience in either high-frequency orderbook research or longer-term signal generation. You will work closely with senior researchers and traders across our global offices (NYC, London, and Hong Kong) to explore market data, develop predictive signals, and build models that directly inform real trading decisions.

This is a full-cycle research role - from data exploration to production deployment, from model development to live performance monitoring - with end-to-end ownership of the research process.

Key Responsibilities

• Analyze market data to uncover patterns, inefficiencies, and predictive signals across different time horizons
• Build and backtest quantitative models using historical market data
• Apply statistical and machine learning techniques - with an emphasis on tree-based methods - to enhance signal quality
• Collaborate with researchers and traders to translate research insights into production-ready strategies
• Develop and maintain data pipelines for large-scale, high-frequency, and time-series market data
• Continuously monitor and iterate on live signals and models based on performance

Qualifications & Requirements

• 2+ years of professional or research experience in high-frequency research and/or longer-term signal generation
• Degree in a quantitative field (Mathematics, Statistics, Computer Science, Physics, Engineering, Financial Engineering, or related)
• 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
• Ability to communicate technical findings clearly to both technical and non-technical audiences
• Familiarity with tree-based methods (e.g., Random Forest, XGBoost, LightGBM) is a plus
• Experience in a proprietary trading or hedge fund environment is a plus
• Experience with C++ or other low-level languages is a plus