Quantitative Researcher
4 weeks ago
About the Role :
In this role, you will design, develop, and implement data-driven quantitative models that drive high-performance trading and investment strategies.
This is an excellent opportunity for someone passionate about financial markets, mathematics, and programming to contribute to real-world portfolio and trading 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 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
(ref:hirist.tech)-
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