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About The Role

We are seeking a highly driven and analytical Quantitative Researcher with a strong foundation in mathematics, statistics, and market microstructure to join our systematic trading team. This role is ideal for candidates passionate about high-frequency trading (HFT), statistical arbitrage, and innovative alpha discovery across global financial markets — equities, futures, options, FX, and commodities.

The ideal candidate should have hands-on experience in developing and testing trading strategies, coupled with a deep understanding of order book dynamics, risk modeling, and ML techniques grounded in sound statistical reasoning, not just generic algorithmic applications.

Key Responsibilities

• Research, design, and implement quantitative trading strategies across global markets using statistical and machine learning models.
• Conduct alpha research, signal generation, and strategy backtesting using large-scale historical tick/order book data.
• Develop and apply statistical arbitrage techniques across multiple asset classes, instruments, and exchanges.
• Model market microstructure phenomena such as latency arbitrage, limit order book dynamics, and short-term price impact.
• Perform rigorous data analysis and hypothesis testing to validate trading ideas and monitor live strategies.
• Collaborate with engineering teams to deploy strategies in production environments with low-latency constraints.
• Continuously monitor and improve model performance using real-time and historical data.
• Stay abreast of latest developments in trading infrastructure, execution technology, and quantitative finance research.
  
  

• Bachelor's, Master's, or PhD in Statistics, Mathematics, Physics, Computer Science, or a related quantitative field.
• Solid knowledge of probability theory, stochastic processes, time series analysis, and optimization.
• Proven experience with global financial markets, including knowledge of exchange mechanics, liquidity provision, and volatility regimes.
• Strong coding skills in Python, C++, or Rust, with experience in numerical computing, data wrangling, and API interaction.
• Familiarity with machine learning techniques rooted in statistical principles (Bayesian methods, Gaussian Processes, feature selection, model validation).
• Experience in handling high-frequency data, order book reconstruction, and building execution algorithms.
• Ability to design robust backtesting frameworks and simulate strategy performance under varying market conditions.
  
  

• Prior experience in a quant fund, HFT firm, or systematic trading desk.
• Familiarity with cloud computing, GPU acceleration, or high-performance computing techniques.
• Exposure to alternative data, non-traditional datasets, and novel signal sources.
• Strong understanding of execution cost modeling, slippage, and latency optimization.