Quantitative Researcher

Anson McCade Pty; United States; On-site; USD 180,000 - 320,000; Full time

Job summary

Anson McCade Pty seeks a Systematic Equity Stat Arb Quantitative Researcher to advance a leading hedge fund's alpha-driven strategies. You will conduct rigorous alpha research, backtesting, and model development across global equity markets, applying machine learning and large-scale data analysis to uncover new signals.

Ideal candidates bring 5+ years in systematic stat arb, a MSc/PhD in a quantitative field, and strong Python/C++ skills, with experience in backtesting and simulation frameworks.

Qualifications

• Advanced degree in a quantitative discipline is required (MSc or PhD).
• Strong foundation in mathematics and statistics with signal generation techniques.
• Proficiency in Python and/or C++ for research and model implementation.

Responsibilities

• Conduct alpha research, backtesting, and implementation of systematic stat arb strategies.
• Design and develop new quantitative trading models across global equity markets.
• Optimize portfolio construction and enhance existing trading strategies.
• Leverage big data and machine learning techniques to uncover new signals.
• Collaborate with researchers, engineers, and portfolio managers in a fast-paced environment.

Skills

Python

C++

Machine learning

Statistical analysis

Backtesting

Education

MSc/PhD in Mathematics/Statistics/CS/Engineering

Tools

Backtesting frameworks

Simulation frameworks

Large-scale data analysis tools

Job description

Systematic Equity Stat Arb Quantitative Researcher

A leading systematic multi-strategy hedge fund is expanding its systematic equity team and is seeking a talented Quantitative Researcher with a proven track record in statistical arbitrage strategy development. This is a unique opportunity to join a high-performing team focused on developing and scaling alpha-driven strategies across global equity markets.

Key Responsibilities

• Conduct alpha research, backtesting, and implementation of systematic stat arb strategies
• Design and develop new quantitative trading models across global equity markets
• Optimize portfolio construction and enhance existing trading strategies
• Leverage big data and machine learning techniques to uncover new signals
• Collaborate with other researchers, engineers, and portfolio managers in a fast-paced environment

Ideal Candidate Profile

• 5+ years of experience developing systematic statistical arbitrage strategies in equity markets
• Advanced degree (MSc/PhD) in a quantitative discipline (e.g. Mathematics, Statistics, Computer Science, Engineering) from a top-tier university
• Strong foundation in mathematics, statistics and signal generation techniques
• Proficient in Python and/or C++ for research and model implementation
• Experience with backtesting, simulation frameworks and large-scale data analysis
• Exposure to machine learning and alternative data is a strong plus