Quantitative Researcher & Engineer
Use Python, C++, NumPy, pandas, scikit-learn, TensorFlow, PyTorch, kdb+/q, Docker, and custom HPC clusters. They design systematic research models across global markets, apply ML to identify predictive signals, and build production research infrastructure for volatility and market structure research.
Free Tailor for ATS: 10/10 runs left
Overview
Successful applicants will focus on quantitative research, or quantitative systems engineering, depending on experience and fit, and collaborate closely across disciplines to advance our research in volatility and market structure.
Key Responsibilities
Quantitative Research
Design and test systematic research models across global markets.
Conduct rigorous statistical analysis using market, fundamental, and alternative datasets.
Apply advanced machine learning (supervised, unsupervised, reinforcement learning) to identify predictive signals.
Evaluate model behaviour and continuously refine research across market conditions.
Quantitative Engineering
Build and optimise research and data infrastructure.
Develop core systems for data ingestion, backtesting, and analytics.
Collaborate with researchers to turn research models into production-ready code.
Continuously enhance system reliability, throughput, and scalability.
Qualifications & Skills
Education: PhD, MSc or BSc in a quantitative field (Mathematics, Physics, Statistics, Computer Science, Engineering).
Quantitative Acumen: Strong background in probability, statistics, linear algebra, time-series analysis, and optimisation.
Programming: Expert-level Python (with NumPy, pandas, scikit-learn) and C++ for performance-critical applications.
System Design: Experience with multi-threaded systems, distributed computing, low-latency optimisation, and Linux environments.
Machine Learning: Proficiency in ML frameworks (PyTorch, TensorFlow) and their application to research problems.
Collaboration: Exceptional communication skills and a team-first mindset. English is the official working language.
Tech Stacks & Tools
Languages: Python, C++
Frameworks & Libraries: NumPy, pandas, scikit-learn, TensorFlow, PyTorch
Databases: SQL, time-series DBs, kdb+/q
Systems: Linux, Git, Docker, high-performance computing environments
Infrastructure: Custom-built HPC clusters, low-latency messaging, real-time data pipelines
Traits We Value
Curiosity, creativity, and a scientific mindset
Ability to work independently and own complex projects end-to-end
Collaborative, open to feedback, and thrives in a high-performance environment
Meticulous attention to detail and commitment to clean, maintainable code
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