Quant Trader

What You'll Do: Identify short-term trading opportunities through analysis of order activity, transaction data, liquidity changes, and relationships between connected markets. Take research from initial hypothesis through data preparation, statistical modeling, historical evaluation, implementation, and live performance monitoring. Improve automated quoting strategies by evaluating execution probability, queue dynamics, trading costs, and the risk of unfavorable price movements. Study exchange behavior and order execution patterns to strengthen strategy decisions and execution quality. Assess how system speed, market data delivery, and connectivity affect trading results. Work with engineers to identify and address performance limitations. Translate research findings into implementation requirements and verify that production behavior matches the intended strategy. Investigate unexpected trading outcomes, data inconsistencies, and model performance changes. What You'll Bring Two or more years of quantitative trading or research experience involving equity index futures within a proprietary trading or HFT environment. Direct experience with CME markets, ideally including ES and/or NQ. Experience analyzing granular exchange data, reconstructing order books, and evaluating order-level activity. A track record of contributing predictive signals or execution models to live, low-latency trading strategies. Understanding of passive execution, order priority, liquidity provision, signal decay, and adverse selection. Strong Python research skills, familiarity with statistical and machine learning methods, and experience working with large datasets. Ability to understand C++ implementations and collaborate with engineers responsible for latency-sensitive trading infrastructure. Helpful Additional Experience Hardware-accelerated trading systems. Relationships between equity futures, cash markets, and index derivatives. Exchange connectivity, network performance, or precision timestamp analysis. #LI-RO1