About the role
We are looking for a highly analytical Quantitative Researcher to join one of the leading HFT/MFT trading firms. The ideal candidate will be responsible for researching, developing, and validating alpha-generating trading strategies using quantitative methods, statistical modeling, and large-scale market data.
Responsibilities:
- Research and develop profitable quantitative trading strategies across multiple asset classes.
- Generate and validate alpha signals using statistical and machine learning techniques.
- Analyze large-scale historical and tick-by-tick market data.
- Design, backtest, and optimize systematic trading strategies.
- Conduct market microstructure research and execution analysis.
- Build predictive models for price movement, liquidity, volatility, and order flow.
- Work closely with Quant Traders and Quant Developers to deploy production-ready strategies.
- Continuously monitor and improve live strategy performance.
Requirements:
- 2-10 years of experience in HFT, MFT, hedge funds, proprietary trading, or quantitative research.
- Strong knowledge of probability, statistics, linear algebra, stochastic processes, and optimization.
- Excellent programming skills in Python; experience with C++, kdb+/q, R, Rust, or Julia is a plus.
- Strong understanding of market microstructure and electronic trading.
- Experience working with large financial datasets and backtesting frameworks.
- Ability to independently research, validate, and improve quantitative models.
Experience in one or more of the following:
- Markets: Indian Markets (NSE, BSE, MCX), US Markets (CME, CBOT, NYMEX, COMEX, NASDAQ, NYSE), European Markets (Eurex, Euronext, ICE).
- Asset Classes: Equities, Futures, Options, Commodities, FX, ETFs, Fixed Income, Crypto.
Preferred Qualifications:
- Bachelor's, master's, or PhD in computer science, mathematics, statistics, physics, engineering, economics, or a related quantitative discipline.
- A strong academic record from Tier-1 institutes (IIT, IISc, ISI, IIIT, BITS, CMI, etc. ) is preferred.
- Experience with machine learning, time-series forecasting, optimization techniques, and quantitative finance is highly desirable.