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 Quantitative Researchers with expertise in quantitative portfolio consturction. 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:
- Conduct research on quantitative portfolio construction and optimization algorithms, with a primary focus on equity portfolios
- Contribute to the design, development, and upkeep of the firm’s portfolio optimization system.
- Explore advanced algorithms for feature engineering, selection, and combination
- Develop both traditional and innovative alpha forecasting techniques and systems
Requirements:
- Strong expertise in optimization theories such as LP, QP, MIP, SOCP, robust optimization, and stochastic optimization, with hands-on experience formulating and solving large-scale portfolio optimization problems
- In-depth knowledge and practical experience with advanced machine learning techniques and their application to quantitative finance.
- Experience with neural networks (NN) and reinforcement learning (RL) in financial applications is a significant advantage
- Comprehensive understanding of quantitative equities portfolio management, including key components like factor models, risk models, and market impact models.
- Proven experience with Python and Java, coupled with strong fundamentals in computer science, including design patterns, concurrency, threading, algorithms, memory management, and data structures
- Proficiency in modern data science toolsets (e.g., Jupyter, pandas, numpy, scipy, sklearn) and machine learning experience is a plus
- A Master’s degree or higher in a quantitative field such as Operations Research, Mathematics, Statistics, Financial Engineering, Computer Science, Electrical Engineering, Physics, or a related discipline
- Detail-oriented, self-motivated, and capable of working effectively both independently and within a small team