Key Responsibility:
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.
