Quant trader

We are looking for a highly analytical Quantitative Trader to develop, test, and execute systematic trading strategies across global markets. The role combines quantitative research, market intuition, and technology to identify alpha opportunities, optimize execution, and manage risk in real time.

Key Responsibilities:

• Research and develop quantitative trading strategies using statistical and machine learning techniques.
• Analyze large market datasets (tick, order book, historical pricing, alternative data) to identify patterns and inefficiencies.
• Build and refine signal generation, portfolio construction, and position-sizing models.
• Execute trades and monitor live strategy performance, slippage, and PnL.
• Partner with quant researchers and developers to productionize strategies.
• Continuously improve execution quality and transaction cost models.
• Perform backtesting and forward testing with robust validation controls.
• Monitor risk limits and ensure compliance with trading and regulatory requirements.
• Investigate strategy drawdowns and implement corrective actions quickly.
• Produce clear performance reports and communicate insights to stakeholders.

Required Qualifications:

• Bachelor’s/Master’s/PhD in Mathematics, Statistics, Physics, Computer Science, Engineering, Finance, or related quantitative field.
• Strong background in probability, statistics, time-series analysis, and optimization.
• Proficiency in Python; strong coding skills in at least one of C++/Java.
• Experience with backtesting frameworks and market microstructure concepts.
• Strong understanding of derivatives, equities, futures, FX, or fixed income markets.
• Ability to make fast, data-driven decisions under pressure.

Preferred Qualifications:

• Experience in high-frequency or low-latency trading environments.
• Familiarity with machine learning for alpha modeling.
• Knowledge of cloud/distributed data infrastructure.
• Prior track record of profitable strategy development and deployment.
• Experience with kdb+/q, SQL, Linux, and real-time data pipelines.