Quantitative Analyst

We are seeking a Quantitative Analyst to join our research team at Motilal Oswal. The ideal candidate will have strong expertise in Python-based backtesting, factor modeling, and low-frequency trading (LFT) strategies. You will play a key role in developing and enhancing systematic trading strategies by leveraging quantitative techniques and data-driven insights.

Key Responsibilities

• Develop and backtest factor models to identify alpha-generating opportunities.
• Design and implement robust back testing backtesting frameworks for LFT strategies using Python.
• Analyze large financial datasets to extract insights and improve model performance.
• Optimize execution strategies and ensure scalability of models.
• Work closely with the fundamental research and trading teams to integrate quant-driven insights.
• Continuously monitor, refine, and enhance existing models based on market conditions.
• Ensure risk management and portfolio construction principles are embedded in strategies.

Key Requirements

• Proficiency in Python, with experience in libraries like Pandas, NumPy, SciPy, and Backtrader/Zipline.
• Strong understanding of factor investing, risk premia strategies, and portfolio optimization.
• Experience in backtesting LFT strategies with robust statistical validation.
• Familiarity with cross-sectional and time-series analysis in financial markets.
• Knowledge of execution algorithms, slippage modeling, and market microstructure is a plus.
• Bachelor's/Masters degree in Quantitative Finance, Statistics, Mathematics, or Computer Science.
• Prior experience in a quant role within a hedge fund, asset management firm, or proprietary trading desk is preferred.

Preferred Skills

• Strong Python programming skills, especially for quantitative research and trading.
• Experience with backtesting frameworks and performance evaluation techniques.
• Fama-French factors and multi-factor modeling expertise.
• Machine learning techniques for financial modeling and prediction.
• Ability to handle high-dimensional datasets and apply statistical methods to financial data.