Quantitative Researcher- New York

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