BlackShip Capital
Quantitative Researcher & Engineer
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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.
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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.
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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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