Quadeye
Quadeye
Quadeye is a leading algorithmic trading firm with its presence across all global exchanges specializing in cutting-edge quantitative strategies and market making. Our team is dedicated to driving innovation in financial markets through advanced statistical models, data science, and algorithmic execution. We pride ourselves on fostering a collaborative environment where technical expertise and creative problem-solving are at the forefront of our trading strategies. We are seeking an exceptional Quantitative Researcher to join our dynamic research team. The ideal candidate will have a strong background in alpha and feature research, statistical modeling, and the end-to-end process of taking models from development to production. This role will involve research, model design and implementation, as well as post-trade analysis to optimize and monetize our trading systems to their fullest potential. Key Responsibilities:
Alpha & Feature Research:
Develop, test, and enhance alpha signals and features using market data and various alternative data sources. Investigate new research areas to identify and extract actionable insights from the market. Statistical Modeling:
Lead efforts in building and refining statistical models to predict market behavior. This includes everything from feature selection and data preprocessing to model selection and validation techniques (e.g., regularization, cross-validation, ensemble methods). Model Combination & Validation:
Explore and implement methods for combining models, leveraging techniques such as model averaging or stacking to improve predictive performance and robustness. Production Implementation:
Work closely with the engineering team to deploy and integrate models into the live trading environment. Ensure that models are optimized for low-latency execution and maintainable in a fast-paced, evolving environment. Post-Trade Analysis:
Perform detailed post-trade analysis to assess model performance and identify areas for improvement. Debug and troubleshoot issues that arise in live trading and contribute to system improvements. System Monetization:
Identify opportunities to improve the profitability of trading strategies through optimization, parameter tuning, and the identification of market inefficiencies. Ensure models are operating at their full potential in real-time markets. Collaboration:
Collaborate with trading and engineering teams to continuously improve research methods, data pipelines, and infrastructure. Share insights and foster a knowledge-sharing culture. Job Location: New York Ideal candidate should have: Strong experience in quantitative research, particularly in alpha and feature development for high-frequency or algorithmic trading Extensive experience in statistical modeling, machine learning, and data analysis techniques Proficiency in programming (Python, C++, R, or similar) with experience in tools like NumPy, SciPy, Pandas, and machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) Experience in taking models into production, with a strong understanding of performance, latency, and system architecture considerations Education:
A degree in a quantitative field such as Mathematics, Statistics, Computer Science, Physics, Engineering, or similar Skills: Excellent problem-solving abilities with a strong mathematical/statistical foundation Experience with time-series analysis, market microstructure, and financial data Ability to analyze trading strategies, debug systems, and implement improvements through detailed post-trade analysis Strong communication skills and the ability to collaborate with cross-functional teams, including traders and engineers Preferred Skills: Experience in high-frequency trading or market-making environments Familiarity with low-latency programming and optimization techniques A proven track record of applying research to drive improvements in live trading systems
Quadeye is a leading algorithmic trading firm with its presence across all global exchanges specializing in cutting-edge quantitative strategies and market making. Our team is dedicated to driving innovation in financial markets through advanced statistical models, data science, and algorithmic execution. We pride ourselves on fostering a collaborative environment where technical expertise and creative problem-solving are at the forefront of our trading strategies. We are seeking an exceptional Quantitative Researcher to join our dynamic research team. The ideal candidate will have a strong background in alpha and feature research, statistical modeling, and the end-to-end process of taking models from development to production. This role will involve research, model design and implementation, as well as post-trade analysis to optimize and monetize our trading systems to their fullest potential. Key Responsibilities:
Alpha & Feature Research:
Develop, test, and enhance alpha signals and features using market data and various alternative data sources. Investigate new research areas to identify and extract actionable insights from the market. Statistical Modeling:
Lead efforts in building and refining statistical models to predict market behavior. This includes everything from feature selection and data preprocessing to model selection and validation techniques (e.g., regularization, cross-validation, ensemble methods). Model Combination & Validation:
Explore and implement methods for combining models, leveraging techniques such as model averaging or stacking to improve predictive performance and robustness. Production Implementation:
Work closely with the engineering team to deploy and integrate models into the live trading environment. Ensure that models are optimized for low-latency execution and maintainable in a fast-paced, evolving environment. Post-Trade Analysis:
Perform detailed post-trade analysis to assess model performance and identify areas for improvement. Debug and troubleshoot issues that arise in live trading and contribute to system improvements. System Monetization:
Identify opportunities to improve the profitability of trading strategies through optimization, parameter tuning, and the identification of market inefficiencies. Ensure models are operating at their full potential in real-time markets. Collaboration:
Collaborate with trading and engineering teams to continuously improve research methods, data pipelines, and infrastructure. Share insights and foster a knowledge-sharing culture. Job Location: New York Ideal candidate should have: Strong experience in quantitative research, particularly in alpha and feature development for high-frequency or algorithmic trading Extensive experience in statistical modeling, machine learning, and data analysis techniques Proficiency in programming (Python, C++, R, or similar) with experience in tools like NumPy, SciPy, Pandas, and machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) Experience in taking models into production, with a strong understanding of performance, latency, and system architecture considerations Education:
A degree in a quantitative field such as Mathematics, Statistics, Computer Science, Physics, Engineering, or similar Skills: Excellent problem-solving abilities with a strong mathematical/statistical foundation Experience with time-series analysis, market microstructure, and financial data Ability to analyze trading strategies, debug systems, and implement improvements through detailed post-trade analysis Strong communication skills and the ability to collaborate with cross-functional teams, including traders and engineers Preferred Skills: Experience in high-frequency trading or market-making environments Familiarity with low-latency programming and optimization techniques A proven track record of applying research to drive improvements in live trading systems