Quantitative Researcher
Quantitative Researcher
Anson McCade3-5 Years
- Posted 3 days ago
- Be among the first 10 applicants
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
- 3+ 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
More Info
Key Skills
large-scale data analysis
backtesting simulation frameworks
signal generation techniques
About Company
Skills:
Ml, Deep Learning, programming, Python, Data Analysis, LLMs, financial data structures, Ai, reinforcement learning, Statistics, time-series analysis, statistical methods
Skills:
Java, Rust, C++, Python, Back testing, On-chain data, Machine-learning methods, Real-time market-data systems, Research infrastructure, Data pipelines, Simulation, Exchange APIs
Skills:
Ml, Big Data, Python, statistical models, Ai
Skills:
Pandas, Excel, Sql, Python, Scikit-learn
Skills:
Python, C++, Deep Learning, modern deep learning architectures, LLM tooling, experiment tracking, regularization, efficient columnar formats, experiment-management tooling, hyperparameter optimization, throughput profiling and optimization, ensembling, machine learning theory, Optimization, large-scale datasets, mixed precision, Statistics, reproducible research environments, large-scale model training, distributed multi-GPU training, CUDA-level optimization, point-in-time-correct dataset construction, streaming data loaders