We are currently working with a tier 1 global hedge fund who is looking to hire an experienced Quantitative Researcher to develop and implement statistical arbitrage, market-neutral, and other systematic equity strategies. The ideal candidate will have a deep understanding of quantitative finance, machine learning, and equities data analysis, with a proven track record of designing and deploying profitable trading models across global equity markets (single stocks, ETFs, indices, and derivatives).
Key Responsibilities:
- Research, design, and backtest statistical arbitrage and quantitative trading strategies (pairs trading, factor-based models, mean-reversion, etc.) for equity markets.
- Analyze large datasets (fundamental data, pricing data, alternative data, order books) to identify robust, non-correlated alpha signals.
- Develop statistical models, machine learning (ML), and AI-driven approaches for predictive analytics and signal generation.
- Optimize execution algorithms for minimizing market impact and transaction costs.
- Collaborate with developers to implement strategies in Python, C++, or R.
- Monitor live trading performance, risk metrics (e.g., factor exposure, VaR), and refine models in real-time.
- Stay updated on equity market microstructure, regulatory changes, and emerging sources of alpha (e.g., alternative data).
- Work closely with traders, engineers, and data scientists to improve research infrastructure and data pipelines.
Required Qualifications:
- 5+ years of experience in quantitative research within equity markets, preferably at a hedge fund, prop trading firm, or asset manager.
- Strong background in mathematics, statistics, and econometrics (multivariate calculus, linear algebra, time-series analysis, Bayesian statistics).
- Proficiency in Python (NumPy, Pandas, SciPy, Scikit-learn) and experience with backtesting frameworks (e.g., custom, Zipline, QuantConnect).
- Deep knowledge of equities data providers (e.g., Bloomberg, Refinitiv, CRSP, Compustat) and familiarity with broker APIs and execution platforms.
- Expertise in statistical arbitrage techniques, factor modeling, and portfolio construction.
- Familiarity with machine learning techniques (supervised/unsupervised learning, feature engineering) applied to financial markets.
- Understanding of equity derivatives (options, futures, swaps) and associated risk management principles.
- Advanced degree (PhD/MS) in Quantitative Finance, Financial Engineering, Computer Science, Physics, Math, Statistics, or a related field.
Preferred Skills:
- Experience with low-latency or high-frequency trading systems (C++, Java, KDB+).
- Knowledge of advanced statistical methods like cointegration, stochastic modeling, and signal processing.
- Experience sourcing, cleaning, and analyzing non-traditional or alternative data sets.
- Published research in quantitative finance or contributions to relevant open-source projects.
