✨ AI Summary
The role is for a mid-to-senior Quant Researcher and Trader responsible for developing and optimizing systematic trading strategies across exchange-traded markets. You will extract predictive signals from market data, improve execution logic, and contribute to production-grade, low-latency trading systems. Key activities include building predictive models, conducting market microstructure research, backtesting strategy pipelines, evaluating costs and liquidity effects, and collaborating with engineers and traders to deploy signals and refine hypotheses. The candidate should have 5+ years of quantitative experience, strong Python and ideally C++ skills, and a solid foundation in statistics and time-series modeling.
Requirements
MSc or PhD in Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or related quantitative discipline- 5–8+ years experience in quantitative research or systematic trading environments- Strong programming skills in Python- Working knowledge of C++ preferred- Strong foundation in probability, statistics, optimization, and time-series modeling- Experience working with market data at scale
Description
About the RoleA trading firm is seeking a mid-to-senior Quant Researcher to develop and optimize systematic trading strategies across exchange-traded markets. This role focuses on extracting predictive signals from market data, improving execution logic, and contributing to production-grade algorithmic trading systems in a low-latency environment.Key ResponsibilitiesAlpha & Signal Research- Develop predictive trading signals using statistical modeling and machine learning techniques- Conduct market microstructure research using tick-level and order-book datasets- Design and test systematic strategies across equities, futures, or derivatives- Analyze signal decay, feature stability, and regime sensitivityBacktesting & Validation- Build scalable back testing pipelines for strategy evaluation- Perform robustness testing across multiple market regimes- Detect overfitting risks and improve model generalization- Evaluate transaction costs, slippage, and liquidity effectsExecution Optimization- Improve execution logic and inventory management models- Support enhancements to quoting strategies in electronic markets- Collaborate with engineers to deploy production-ready signals- Optimize latency-sensitive components where requiredCross-Team Collaboration- Work alongside traders to refine strategy hypotheses- Partner with engineering teams on implementation workflows- Contribute to internal research tools and analytics frameworks