DRW is a technology-driven, diversified principal trading firm. We trade our own capital at our own risk, across a broad range of asset classes, instruments and strategies, in financial markets around the world. As the markets have evolved over the past 25 years, so has DRW – maximizing opportunities to include real estate, cryptoassets and venture capital. With over 1000 employees at our Chicago headquarters and offices around the world, we work together to solve complex problems, challenge consensus and deliver meaningful results. It’s a place of high expectations, deep curiosity and thoughtful collaboration.
As a Quantitative Researcher, you will join a small trading team with a macro-RV mandate. You will be tasked with solving challenging problems arising in a trading environment while utilizing statistical and machine learning techniques as the group continues to research and deploy new strategies. You will join a team with a start-up feel, and work directly with experienced traders that are intellectually curious across the spectrum of finance, mathematics, and technology.
How you will make an impact:
- Formulate and apply mathematical modeling and quantitative techniques such as machine learning or econometrics to systematically identify and monetize trading opportunities across markets, with an emphasis on global fixed income
- Work closely with traders and researchers to build, automate and improve existing trading and monitoring tools, strategies and research infrastructure
- Keep abreast of the latest academic and market research in order to advance the groups research agenda
What you bring to the team:
- Degree in a technical discipline from a top university with a focus on statistics, machine learning, econometrics, or related fields, and a GPA > 3.7/4
- 1-2 years of full-time professional experience or multiple internships as a Quantitative Researcher applying statistics and machine learning techniques to real-world datasets, preferably in the financial markets (though strong candidates in tech will also be considered)
- Proficiency in Python programming using the machine learning stack: numpy, pandas, scikit-learn, keras, tensorflow, pytorch, etc.
- Solid mathematical and analytical ability, including in-depth knowledge of statistics; working knowledge of standard ML algorithms, including SVMs, random forests, boosting, k-means, neural networks, etc.
- Self-motivated and able to work collaboratively and productively with others
- Strong sense of accountability and desire to continuously learn
- Excellent written and verbal communication skills to report research results as well as methodologies
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