Greenwich, CT30+ days ago
B.S. degree from a top institution in computer science, engineering, mathematics, statistics, operations research, physics, or another quantitative discipline • Academic or practitioner experience in machine learning methodologies preferred • 2-5 years' experience working in a data-driven research environment with an alpha focus • Experience in quantitative research at a top asset manager or hedge fund preferred • Proficient programming in Python required • Experience with translating mathematical models and algorithms into code • Ability to manipulate large financial data sets for empirical research and handle complex data • Experience with machine learning software libraries such as scikit-learn, TensorFlow, or PyTorch • Experience with natural language processing technology, including LLMs and prompt engineering is a plus • Strong quantitative skills with demonstrated understanding of mathematics, probability, statistics, and linear algebra • Nuanced understanding of economic and financial concepts and demonstrated intuition around applying these concepts in a quantitative environment • Ability to work independently as well as part of a team • Demonstrated ability to express and articulate ideas and thought processes in both verbal and written form. Responsibilities: • Engage in alpha research and other quantitative analysis to improve current investment strategies in collaboration with existing research team • Perform statistical and economic research using traditional financial and alternative data to develop new alpha signals • Successful researchers manage, in collaboration with supervising portfolio manager, all aspects of the research process, including data ingestion and processing, data analysis, methodology selection, implementation, and testing, prototyping, and performance evaluation • Engage in building, training, and fine-tuning machine learning architectures for cross-sectional or time-series analysis • Learn how to use the appropriate model for the problem at hand • Construct economically nuanced features from raw data and adjust or develop new frameworks to evaluate their effectiveness • Engage with most recent academic and practitioner literature in the field • Occasionally, conduct research on various aspects of the implementation of investment strategies, such as trading cost models, risk models, optimization, and portfolio construction • Add features to proprietary research system to implement new research ideas.