Key Responsibilities :

- Research, design, and implement systematic trading strategies across multiple asset classes (equities, derivatives, fixed income, etc.

- Conduct in-depth statistical and econometric analysis on financial data to discover predictive signals (alpha).

- Use historical and real-time data to backtest strategies under various market conditions.

- Develop financial models using advanced mathematical and statistical techniques such as machine learning, time-series forecasting, factor modeling, etc.

- Analyze risk-adjusted returns and optimize strategies based on drawdown, Sharpe ratio, volatility, etc.

- Work with large, complex datasets using Python, R, or C++ to clean, process, and derive insights.

- Automate data collection, data transformation, and model calibration pipelines.

- Use SQL/NoSQL databases or big data tools for data management and retrieval.

- Collaborate with traders, portfolio managers, and data engineers to refine strategies and implement research findings in production.

- Translate complex quantitative findings into actionable insights and communicate them effectively to non-technical stakeholders.

- Continuously monitor model performance and suggest improvements based on live data and market shifts.

Required Skills & Qualifications :

- Education : Bachelor's or Masters degree in Quantitative Finance, Mathematics, Statistics, Physics, Computer Science, Engineering, or a related quantitative field.

- Proficiency in at least one of the following : Python, R, C++, or MATLAB.

- Strong understanding of probability, statistics, stochastic processes, linear algebra, optimization, and econometrics.

- Familiarity with financial instruments, portfolio construction, and market microstructure.

- Experience with SQL, NoSQL, or distributed data processing platforms (e. , Hadoop, Spark) is a plus.

- Experience with any backtesting tools such as Zipline, QuantConnect, Backtrader, or custom frameworks is advantageous