Quantitative Researcher

• 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.

• 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.

• 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.

• 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

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

• 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.

• 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.

• 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.

• 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.

Skills