Quantitative Researcher (MFT Team)

Pace Stock Broking Services is looking for a highly analytical and passionate Quantitative Researcher to join our Medium Frequency Trading (MFT) Team. The ideal candidate will be responsible for researching, developing, testing, and optimizing systematic trading strategies across Indian financial markets. This role offers an opportunity to work closely with experienced traders, developers, and quantitative professionals to build scalable and profitable trading models.

Key Responsibilities

• Research, design, and develop quantitative trading strategies for Indian Equity, Futures & Options markets.
• Analyze large-scale market and tick-level datasets to identify alpha-generating opportunities.
• Build robust backtesting frameworks and evaluate strategy performance.
• Develop statistical models for market prediction, signal generation, and execution optimization.
• Collaborate with developers to implement production-ready trading strategies.
• Continuously monitor live strategies and optimize them based on market behavior.
• Perform risk analysis and enhance portfolio performance using quantitative techniques.
• Stay updated with evolving market microstructure and new quantitative methodologies.

• Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Engineering, Economics, Finance, or a related quantitative discipline.
• 2–4 years of experience in Quantitative Research, Algorithmic Trading, or Systematic Trading.
• Strong understanding of Equity, Futures & Options markets, with exposure to Index Options preferred.
• Excellent knowledge of probability, statistics, linear algebra, and optimization techniques.
• Strong programming skills in Python.
• Experience with C++ is an added advantage.
• Experience working with historical market data, backtesting, and strategy evaluation.
• Familiarity with Pandas, NumPy, SciPy, SQL, and Git.
• Strong analytical thinking and problem-solving skills.

• Experience in developing Medium Frequency Trading (MFT) or systematic trading strategies.
• Knowledge of market microstructure and order book dynamics.
• Exposure to machine learning techniques in financial markets.
• Experience with factor research, signal generation, and portfolio optimization.
• Understanding of transaction costs, slippage, and execution models.

• Opportunity to work on cutting-edge quantitative trading strategies.
• Exposure to high-performance trading infrastructure.
• Collaborative environment with experienced traders and engineers.
• Fast-paced learning and career growth.
• Competitive salary and performance-based incentives.