Quantitative Researcher, Commodity Options at DRW
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 900 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 have an opportunity to solve challenging problems arising in a trading environment while utilizing statistical scientific algorithms and machine learning techniques. You will find great minds with diverse backgrounds, who are passionate about cultivating new ideas and exploring ways to bring them to life. Our quantitative researchers work closely with experienced traders and software engineers.
How you will make an impact:
- Create practical solutions to problems presented in the trading environment
- Conduct statistical analysis of market data, historical trends, and relationships across multiple asset classes
- Formulate and apply mathematical modeling, quantitative methods and machine learning techniques to identify and capture trading opportunities of outright volatility, term structure of volatility, and skew
- Predict price and volatility movements using statistics, signal processing and machine learning
- Work closely with traders and researchers to build and refine trading tools, strategies, research infrastructure
What you bring to the team:
- Have completed a Master’s or PhD in a technical discipline with a focus on Statistics, Optimization, Machine Learning, Artificial Intelligence, Quantitative Finance or related fields
- Proficiency in Python programming experience using the Python machine learning stack: numpy, pandas, scikit-learn, etc.
- Proficient programming skills with experience exploring large datasets
- Strong analytical and problem-solving skills including a solid foundation of statistics knowledge
- Solid mathematical and analytical ability; exceptional problem-solving and modeling ability
- Working knowledge of probability theory, derivatives prcing, stochastic calculus and numerical algorithms such as finite differences, Monte Carlo simulation, 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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