Quantitative Researcher, Equities

About Us

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it's not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

The Role

Algorithmic Trading Researchers at DRW apply tools from a variety of disciplines including statistics, control theory, machine learning, optimization, and signal processing to develop algorithmic trading strategies. Our research team collaborates on idea generation and strategy development, while encouraging independent exploration and original approaches. Researchers have access to clean data integrated with a high performance-computing grid and the support of dedicated software developers.

Responsibilities

• Research and develop intraday statistical arbitrage strategies in equities
• Analyze high-frequency market data to identify short-term predictive signals
• Build and evaluate models for intraday alpha, risk, transaction costs, liquidity, and portfolio construction
• Collaborate with traders and engineers to implement, monitor, and improve systematic trading strategies in production
• Apply rigorous statistical testing, simulation, and performance attribution to validate signals and trading performance
• Create and refine high-quality predictive signals using statistics, machine learning, and signal processing techniques
• Identify and mathematically characterize inefficiencies in financial markets
• Apply NLP and other cutting-edge methods to uncover alpha in non-traditional datasets
• Utilize advanced optimization techniques to design and construct optimal portfolios
• Design and implement automated trading agents to achieve superior execution performance
• Formulate research problems, conduct rigorous analysis, and transition ideas into fully functional trading systems

Requirements

• 4+ years of professional experience in equity/futures statistical arbitrage or systematic trading research
• Advanced degree in a quantitative field with a focus on statistics, mathematics, machine learning, signal processing or optimizations
• Experience in handling large datasets using languages such as Python or C++
• Significant hands-on experience with formulating a research problem, conducting the research and developing a working system
• Proven track record in delivering successful systematic strategies
• Self-starter with strong proactivity, sets ambitious goals, willingness to drive and own projects, and proactively identifies opportunities for impact
• Excellent verbal and written communication skills
• Meticulous attention to details and accuracy in work