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.

Required Qualifications

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.

Preferred Qualifications

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.