Equity Statistical Arbitrage Quantitative Researcher
5 days ago
Overview
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).
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.
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Other
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