Quant Researcher - Equities Statistical Arbitrage

Start up hedge fund specialized in mid-frequency quantitative strategies across equities, futures, and cryptocurrencies is seeking a quantitative researcher to join the team. Their strategies combine rigorous quantitative research, high-quality data infrastructure, and fully systematic execution.

As a Quantitative Researcher in Equities Statistical Arbitrage, you will be responsible for the entire lifecycle of systematic trading strategies. You will leverage massive alternative and market datasets to extract predictive signals, build robust mathematical models, and optimize portfolio execution.

Responsibilities

• Alpha Generation: Research, develop, and backtest innovative predictive signals (alphas) for medium-to-high frequency equities statistical arbitrage strategies.
• Data Analysis: Analyze large, unstructured, and alternative datasets (e.g., corporate filings, consumer data, sentiment analysis, order book dynamics) to find novel sources of market inefficiencies.
• Portfolio Optimization: Improve existing portfolio construction, risk modeling, and transaction cost analysis (TCA) frameworks to maximize risk-adjusted returns.
• Strategy Maintenance: Monitor, evaluate, and refine live trading strategies, ensuring they perform within expected risk parameters.
• Collaboration: Partner with core infrastructure engineers to productionize research code and optimize execution algorithms.

Qualifications

• Education: Ph.D. or Master’s degree in a highly quantitative discipline (e.g., Computer Science, Mathematics, Physics, Statistics, Operations Research).
• Experience: 3+ years of professional experience as a Quantitative Researcher focusing on systematic equities, specifically within statistical arbitrage, pairs trading, or electronic market making.
• Programming: Advanced proficiency in Python (including Pandas, NumPy, SciPy) and/or C++. Experience with SQL and handling big data infrastructure (e.g., Spark, Hadoop) is highly valued.
• Mathematical Background: Deep understanding of time-series analysis, machine learning algorithms, linear algebra, and stochastic processes.
• Domain Knowledge: Strong grasp of equity market microstructure, corporate actions, and multi-factor risk models (e.g., Barra, Axioma).
• Mindset: Highly analytical, detail-oriented, and a self-starter who enjoys solving open-ended, complex financial problems.