Portfolio Manager

Bitqcode quantitative capital; Dubai; On-site; AED 250,000 - 420,000; Full time

Job summary

Bitqcode Quantitative Capital is seeking a Portfolio Manager to independently research, develop, deploy, and manage profitable trading strategies across global markets. You will own the complete investment lifecycle from idea generation and portfolio construction to execution and continuous optimization.

This high-ownership role requires a track record in profitable systematic strategies, strong quantitative skills, and collaboration with engineering and research teams to maintain cutting-edge

Qualifications

• 5+ years managing systematic or quantitative investment strategies in hedge funds, proprietary trading firms, or institutional asset managers.
• Strong understanding of portfolio construction, risk-adjusted returns, factor investing, and capital allocation.
• Experience building and managing live quantitative trading strategies with measurable performance.
• Deep understanding of derivatives including futures, options, swaps, and leverage.
• Strong statistical and mathematical foundation including probability, optimization, time series analysis, and machine learning concepts.
• Excellent programming skills in Python, with experience using quantitative research libraries and backtesting frameworks.
• Ability to analyze large financial datasets and extract robust, production-ready trading signals.
• Strong knowledge of market microstructure, execution quality, transaction costs, and liquidity.
• Experience evaluating strategy robustness through out-of-sample testing, walk-forward validation, and stress testing.
• Ability to communicate complex quantitative ideas clearly to engineers, researchers, and leadership.

Responsibilities

• Portfolio construction across multiple asset classes balancing risk return diversification and capital efficiency.
• Alpha generation through quantitative research statistical modeling and systematic strategy development.
• End-to-end ownership of strategies from research and backtesting through production deployment and ongoing monitoring.
• Risk management including exposure limits drawdown controls leverage management and portfolio stress testing.
• Performance attribution and analysis to identify drivers of returns inefficiencies and improvement opportunities.
• Capital allocation across strategies based on performance capacity and market conditions.
• Collaboration with engineering teams to improve execution quality trading infrastructure and research tooling.
• Market monitoring during major macro events and adapting portfolio exposures when necessary.
• Continuous research into new markets datasets execution techniques and quantitative signals.
• Mentoring junior researchers and traders while helping establish investment and research best practices.

Skills

Quantitative research

Portfolio construction

Risk management

Python programming

Backtesting

Machine learning

Communication with engineers

Tools

FIX connectivity

Trading infrastructure

Backtesting frameworks

Job description

Bitqcode Quantitative Capital is a quantitative hedge fund operating fully automated trading strategies across global markets 24 7 We build systematic strategies across equities futures options FX commodities and macro products using large-scale research machine learning and quantitative modeling We re looking for a Portfolio Manager who can independently research develop deploy and manage profitable trading strategies You ll own the complete investment lifecycle from idea generation and portfolio construction to execution monitoring and continuous optimization This is a high-ownership role where your decisions directly impact fund performance.

What you ll own

• Portfolio construction across multiple asset classes balancing risk return diversification and capital efficiency
• Alpha generation through quantitative research statistical modeling and systematic strategy development
• End-to-end ownership of strategies from research and backtesting through production deployment and ongoing monitoring
• Risk management including exposure limits drawdown controls leverage management and portfolio stress testing
• Performance attribution and analysis to identify drivers of returns inefficiencies and improvement opportunities
• Capital allocation across strategies based on performance capacity and market conditions
• Collaboration with engineering teams to improve execution quality trading infrastructure and research tooling
• Market monitoring during major macro events and adapting portfolio exposures when necessary
• Continuous research into new markets datasets execution techniques and quantitative signals
• Mentoring junior researchers and traders while helping establish investment and research best practices

Strongly preferred

• Experience managing multi-strategy portfolios
• Track record of profitable systematic strategies deployed in live markets
• Experience across global futures equities FX commodities or options markets
• Knowledge of market making statistical arbitrage volatility trading or high-frequency trading
• Experience using alternative datasets and machine learning in production research
• Familiarity with execution algorithms FIX connectivity and institutional trading infrastructure
• Experience working alongside quantitative engineering and infrastructure teams

How you operate

• You think in probabilities not predictions
• Every investment thesis is backed by data statistics and rigorous testing
• Risk management comes before return maximization
• You continuously challenge your own assumptions and improve existing strategies
• You care about repeatable investment processes rather than one-off wins
• You focus on long-term Sharpe ratio not short-term P amp L swings

5+ years managing systematic or quantitative investment strategies in hedge funds, proprietary trading firms, or institutional asset managers.

Strong understanding of portfolio construction, risk-adjusted returns, factor investing, and capital allocation.

Experience building and managing live quantitative trading strategies with measurable performance.

Deep understanding of derivatives including futures, options, swaps, and leverage.

Strong statistical and mathematical foundation including probability, optimization, time series analysis, and machine learning concepts.

Excellent programming skills in Python, with experience using quantitative research libraries and backtesting frameworks.

Ability to analyze large financial datasets and extract robust, production-ready trading signals.

Strong knowledge of market microstructure, execution quality, transaction costs, and liquidity.

Experience evaluating strategy robustness through out-of-sample testing, walk-forward validation, and stress testing.

Ability to communicate complex quantitative ideas clearly to engineers, researchers, and leadership.