Head of Quant Trading

Key Responsibilities

• Quantitative Strategy Development & Research
• Lead the creation and refinement of proprietary trading algorithms rooted in the firm’s market framework, leveraging advanced statistical and machine-learning techniques.
• Build forecasting, signal-generation, and risk models; run rigorous back-tests and simulations to validate performance.
• Mine large, heterogeneous datasets (market microstructure, alternative data, etc.) for actionable insights.
• Continuously evaluate emerging research (deep learning, reinforcement learning, agent-based modeling) to sharpen our edge.
• Technical Infrastructure & Implementation
• Partner with engineering to design high-throughput trading systems that scale globally.
• Oversee codebases in Python, and C++; enforce best practices for testing, CI/CD, and performance monitoring.
• Build end-to-end pipelines for data ingestion, model training, and live deployment; ensure seamless connection to execution venues and data feeds.
• Select and integrate best-in-class analytics platforms, databases, and cloud resources.
• Performance Analysis & Risk Management
• Define and track KPIs (alpha decay, slippage, Sharpe, drawdown, and latency) via real-time dashboards.
• Embed robust risk models and dynamic hedging; enforce firm-wide limits and compliance requirements.
• Iterate relentlessly: parameter sweeps, sensitivity analyses, and scenario tests to future-proof strategies.
• Collaboration & Leadership
• Grow and mentor a multidisciplinary team of quants, data scientists, and engineers; cultivate a culture of experimentation and peer review.
• Champion readable, well-tested, version-controlled code and transparent research notebooks.

Qualifications

• Education: B.S. or M.S. in a quantitative field such as Mathematics, Computer Science, Engineering, Statistics, or Physics.
• Experience: Minimum 2 years building and deploying profitable algorithmic strategies at a hedge fund, bank, or proprietary trading firm.
• Programming: Advanced expertise in at least one core language (Python, C++, or Java) and familiarity with Linux, Git, and CI workflows.
• Data Science: Deep knowledge of statistical modeling, and machine-learning frameworks (PyTorch, TensorFlow, scikit-learn).
• Systems: Proven skill in real-time data pipelines, distributed/cloud computing, and performance optimization.
• Language: Fluent English (written and spoken) is required.
• Soft Skills: Exceptional analytical rigor, clear communication, and the leadership mindset to help build a high-performance team from scratch.
• Deep and careful thinking but still able to progress and iterate quickly