Our client is one of the world’s premier investment firms. The firm deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures, and foreign exchange. The core of this effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.
 
Role/Responsibilities:

• Research and develop automated, rigorous, and innovative anomaly detection methods
• Develop models to explain unusual patterns or events
• Apply new models to data processing and trading activity monitoring infrastructure
• Conduct signal generation research

• Collaborate with colleagues to transform intuitions into rigorous research methodology

Requirements:

• MS or PhD in statistics, engineering, applied math, computer science or other quantitative field with a strong foundation in statistics
• 2+ years of work experience at a financial services firm
• Demonstrated proficiency in Python, SQL R, or C/C++
• Familiarly with data science toolkits, such as scikit-learn, Pandas, keras, and tensorflow
• Strong command of foundations of applied and theoretical statistics, linear algebra and vector manipulation, and machine learning techniques
• Understanding of the nuances and pitfalls of common models and modeling approaches, such as analyzing time-series based data vs. other types
• Ability to quickly and efficiently scrub, format, and manipulate large, raw data sources
• Strong knowledge of financial markets, instruments, and modeling/valuation is a plus

• Interest in experimenting with new types of data visualization is a plus