Systematic Equities Quantitative Researcher

Location New York
Discipline: Hedge Funds & Proprietary Trading
Job type: Permanent
Contact name: Ilayda Gwillim

Contact email: ilayda.gwillim@venturesearch.com
Job ref: 2955
Published: 7 days ago
Systematic Equities Senior Quantitative Researcher
New York

 
Venture Search is partnering with a leading hedge fund renowned for its technology and industry-leading infrastructure looking to expand aggressively throughout 2025.
 
The firm is seeking systematic-equity Quantitative Researchers with expertise in Equities strategies. This opportunity is ideal for individuals at senior level who are looking to join a dynamic firm with cutting-edge infrastructure.
 
You would play a key role in driving growth in a rapidly expanding team in New York. Their primary focus is on developing and implementing mid-frequency equities strategies.

Role:
  • Take a hands-on leadership role and drive innovation in one or more research areas, including statistical alpha, fundamental alpha, advanced statistical (machine) learning methods, and portfolio construction.
  • Actively contribute to the development and review of the team’s research agenda, collaborating with team members to explore new research directions, data sources, and strategies.
  • Oversee the entire research process for each project, from idea generation and model design to signal testing and pre-production implementation.
  • Mentor junior team members by providing guidance on research, technical training, and career development.
 
Requirements:
  • At least 3 years (ideally 5+ years) of experience in buy-side quantitative research, specializing in statistical arbitrage or quantamental strategies. Experience creating alpha models for intraday, daily, weekly, or monthly timeframes is highly regarded.
  • Prior experience as a quant portfolio manager or sub-portfolio manager with a proven track record is a plus, though not a requirement
  • Experience building and scaling large research and back testing platforms is highly valued.
  • Extensive knowledge of various quantitative datasets and data providers.
  • Proficiency in Python programming, with substantial experience in scientific computing and machine learning libraries
  • Academic and professional expertise in applying modern machine learning techniques to quantitative finance is a strong advantage
  • Familiarity with other programming languages such as SQL, Java, C++, or Matlab is advantageous
  • Strong communication and leadership abilities, with a track record of working effectively with diverse teams and individuals at different experience level
  • A Master’s degree or higher in Mathematics, Statistics, Computer Science, Physics, Financial Engineering, or related fields, with a solid foundation in mathematics and statistics. A PhD and academic research experience are highly desirable