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Quantitative Research Engineer

2+ years experience

About the Role

At GalacticFlow Research, a proprietary high-frequency trading (HFT) firm, we develop and execute sophisticated trading strategies powered by cutting-edge technology and quantitative research. We are looking for a Quantitative Research Engineer to help design, implement, and optimize trading algorithms that operate at ultra-low latencies.

Responsibilities

  • Develop, test, and optimize quantitative trading strategies using real-time and historical market data.
  • Conduct data-driven research and statistical analysis to identify alpha signals and improve execution efficiency.
  • Implement low-latency and high-performance trading models in Python and C++
  • Collaborate with traders, quants, and engineers to deploy and enhance trading strategies in live markets.
  • Design and maintain market data pipelines for tick-data replay, backtesting, and analytics.
  • Optimize order execution by analyzing slippage, market impact, and order book dynamics.
  • Work on portfolio risk management, strategy PnL evaluation, and execution performance metrics.

Requirements

  • 2+ years of experience in quantitative research, trading, or financial engineering.
  • Strong proficiency in Python and C++, with a focus on performance optimization.
  • Deep understanding of probability, statistics, and machine learning for financial modeling.
  • Experience with market microstructure, order book dynamics, and execution algorithms.
  • Familiarity with SQL and time-series databases for analyzing large datasets.
  • Strong problem-solving skills and algorithmic thinking, with the ability to work in a fast-paced, high-stakes environment.
  • Bachelor's, Master's, or PhD in a quantitative field (Computer Science, Mathematics, Statistics, Physics, or Engineering). Candidates with backgrounds from IITs are highly preferred.
  • Strong competitive coding profile on platforms like Codeforces or AtCoder is a plus.

Bonus

  • Experience in crypto trading, HFT, or market-making strategies.
  • Knowledge of options pricing, statistical arbitrage, or reinforcement learning for trading.
  • Exposure to low-latency networking and multi-threaded systems.

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