Quantitative Researcher

In this role, you will drive quantitative research and the development of systematic trading strategies, with a primary focus on equity markets. You will be responsible for generating new ideas and designing systematic trading strategies using statistical and quantitative methods, as well as assessing the feasibility and scalability of research outputs.

You will analyze both historical and real-time market data to identify patterns and predictive signals, and design and prototype quantitative models using a Python-based framework. This includes running backtesting, simulation, and stress testing to validate model performance. Furthermore, you will collaborate with engineers and traders to bring research ideas into production, contributing to building a strong research culture and best practices across the team.

Quantitative Research &Strategy Development

• Research market behavior and trading opportunities, with a primary focus on equity markets.
• Generate new ideas and design systematic trading strategies using statistical and quantitative methods.
• Assess the feasibility and scalability of research outputs.

Data Analysis & Signal Research

• Analyze both historical and real-time market data to identify patterns and predictive signals.
• Test the statistical significance and robustness of research findings.
• Explore under-utilized segments of the equity market in search of hidden alpha.

Model Design & Validation

• Design and prototype quantitative models using a Python-based research framework.
• Run backtesting, simulation, and stress testing to validate model performance.
• Evaluate model stability, risk characteristics, and production readiness.

Research Workflow & Collaboration

• Contribute to building reproducible, scalable research workflows.
• Work with engineers and traders to bring research ideas into production.
• Share perspectives and help reinforce a strong research culture and best practices across the team.

Qualifications:

Education:

• Bachelor's or Master's degree in Mathematics, Physics, Statistics, Computer Science, Engineering, Economics, or a related quantitative field.

Experience:

• Experience in quantitative research, systematic trading, or financial modeling.
• Experience from a proprietary trading firm, hedge fund, or systematic trading team is a strong plus.
• Experience working with high-frequency, high-dimensional, or out-of-core financial data is an advantage.
• Full ML-lifecycle experience — data generation, model calibration, validation, deployment, and live monitoring.

Basic Qualifications:

• Strong foundations in probability, statistics, optimization, and numerical methods.
• Advanced Python for quantitative research, simulation, and numerical programming.
• C++ (or C / C#) basics for working with low-latency production systems.
• Strong computer-science fundamentals (data structures, algorithms, complexity).
• Able to manage multiple parallel research projects independently with strong ownership.

Skills:

• Quantitative modeling, statistical analysis, and machine learning.
• Python data/scientific stack (Pandas, NumPy, SciPy, scikit-learn).
• Deep learning and gradient-boosting frameworks (PyTorch, TensorFlow, XGBoost, CatBoost).
• Time-series modeling and feature engineering on financial data.
• Backtesting, simulation, and benchmarking frameworks. Order-book analysis.
• Algorithmic trading strategy design and execution logic.
• Collaboration with low-latency engineering and trading teams.
• Strong analytical and problem-solving ability.
• Clear technical communication and documentation.

Personality:

• Analytical, scientific, and detail-oriented.
• Self-driven, with full ownership from idea to live result.
• Curious and creative, eager to explore new model architectures and techniques.
• Calm and resilient under pressure in a fast-moving, high-risk trading environment.
• Collaborative and communicative across cross-functional teams (researcher, engineer, trader).
• Comfortable with short feedback cycles and rapid iteration.