Portfolio Manager – Quantitative Hedge Fund

About the role

Bitqcode Quantitative Capital is a fully automated quantitative hedge fund that trades 24/7 across global markets. We are seeking a Portfolio Manager who will independently research, develop, deploy, and manage systematic trading strategies that directly impact fund performance.

Key responsibilities

• Design and construct multi‑asset portfolios, balancing risk, return, diversification and capital efficiency.
• Generate alpha through quantitative research, statistical modeling and systematic strategy development.
• Own the end‑to‑end lifecycle of strategies: research, back‑testing, production deployment, monitoring and continuous optimization.
• Implement risk management controls including exposure limits, drawdown caps, leverage management and stress testing.
• Perform performance attribution and analysis to identify drivers of returns and improvement opportunities.
• Allocate capital across strategies based on performance, capacity and market conditions.
• Collaborate with engineering teams to enhance execution quality, trading infrastructure and research tooling.
• Monitor markets during major macro events and adjust portfolio exposures as needed.
• Mentor junior researchers and traders, promoting best practices in investment and research.

Required profile

• 5+ years of experience managing systematic or quantitative investment strategies in hedge funds, prop‑trading firms or institutional asset managers.
• Proven track record of building and managing live quantitative trading strategies with measurable performance.
• Deep understanding of derivatives (futures, options, swaps) and leverage.
• Strong statistical and mathematical foundation including probability, optimization, time‑series analysis and machine learning concepts.
• Excellent programming ability in Python and familiarity with quantitative research libraries and back‑testing frameworks.

Required skills

• Python programming
• Quantitative research libraries (e.g., NumPy, pandas, scikit‑learn)
• Back‑testing frameworks
• Statistical modeling and optimization
• Time‑series analysis
• Machine‑learning techniques for signal generation
• Large‑scale financial data handling
• Knowledge of market microstructure and execution quality