Manager of CTA Intraday Monetization

On behalf of an investment management firm, oversee and manage the formulation of mathematical models for investment strategies based on independent quantitative analysis, performing the following duties: Identify suitable objective functions for fitting parameters to enhance trading strategies. Manage the implementation and improvement of trading strategies; analyze existing strategy behavior and propose and implement improvements using KDB/Q and Python. Direct the implementation and use of computer algorithms to solve resulting optimization problems. Guide and direct team in employing mathematical models and statistical techniques, including regression analysis, machine learning algorithms, and statistical inference, and apply financial knowledge and computer skills to enhance investment strategies for futures and other asset classes. Lead the modeling and analysis market impact and control trading costs of systematic futures and FX trading strategies based on knowledge of market structure. Lead new trading ideas pitches to senior management. Lead sophisticated analyses to reveal previously unknown statistical effects, assess their robustness, and implement systematic strategies to monetize such effects. Manage testing and validation of both simulation and live trading applications. Oversee application development utilizing Shell scripts and Python to automate daily routines for processing data in support of trading strategies. Requirements:Masters Degree in Business Analytics, Mathematical Finance, or Financial Engineering, plus 3 years of experience in position offered or as Quantitative Researcher.* *All required experience must have included: applying quantitative research, statistical modeling, machine learning and predictive analytics in large-scale data analysis; programming in Python and K/QDB to design analytical applications, data pipelines and automation tools; developing and implementing Bayesian models, Monte Carlo simulations, forecasting methodologies, scenario analyses and hypothesis testing frameworks; acquiring, processing and analyzing structured and unstructured alternative data sets; building AI-powered research systems including autonomous and multi-agent frameworks; and designing cloud-based data pipelines and AI applications leveraging Large Language Models (LLMs), ChatGPT, Gemini, and LangChain. #LI-DNI.