Quantitative Researcher

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. 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. 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.