High Frequency Quant Trader

Our client 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. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, they trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. They have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. The Team: You will join a trading team responsible for managing systematic strategies in Equities. The team focuses on both latency sensitive and non-latency sensitive investment opportunities across geographies and holding periods. The team is responsible for the complete lifecycle of quantitative investment process, research, development, and trading of systematic strategies. The team strongly emphasizes cutting-edge innovative scientific research and is looking to add an individual who is enthusiastic about contributing within a team environment. Responsibilities: The main responsibility of the role will be to research, design and implement new quantitative trading strategies. This will entail generating alphas from a variety of traditional and alternative datasets using rigorous statistical methods. To be successful in this role, the ideal candidate will need to build a deep understanding of the underlying datasets and be able to apply the latest scientific algorithms for statistical model development. Qualifications: The ideal candidate will be excited about working in a collaborative team environment, with an emphasis on team performance. We also require the following: 5+ years’ experience in quantitative investment research in High Frequency Trading in the US, Canadian, or European Markets is required Excellent written and verbal communication skills to report research results/methodologies required Strong programming skills with the ability to explore large datasets required #J-18808-Ljbffr