Quantitative Researcher

Asia/Bangkok — daily overlap with Warsaw 14:00–18:00 BKK

Conduct research, design, and validate quantitative trading models and strategies — with a primary focus on equity markets. Generate new ideas, raise the firm's predictive-modeling standards, and help maintain a world-class research environment that turns financial data into actionable insight.

What you'll do

• •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.
• •Analyze both historical and real-time market data to identify patterns and predictive signals. Test the statistical significance and robustness of research findings.
• •Design and prototype quantitative models using a Python-based research framework. Run backtesting, simulation, and stress testing to validate model performance.
• •Contribute to building reproducible, scalable research workflows. Work with engineers and traders to bring research ideas into production.

What we're looking for

• •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.
• •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.
• •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.

Nice to have

• •Full ML-lifecycle experience — data generation, model calibration, validation, deployment, and live monitoring.
• •Experience with high-frequency, high-dimensional, or out-of-core financial data.