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We’re seeking a talented Systematic Portfolio Manager, who is open to relocating to Oman to join a rapidly expanding investment management firm. This is a high-impact role for someone who wants real ownership, fast responsibility, and the opportunity to build and run systematic portfolios in a dynamic, entrepreneurial environment.
Job Responsibility:
Take over and systematize multiple existing sub-portfolios within 3 months, including signal integration, risk controls, and performance analytics
Lead full-cycle development of quantimental investment systems-from hypothesis and empirical testing to production deployment
Build modular, production-quality Python codebases using modern analytics, ML, and statistical tools
Conduct research across econometrics, machine learning (RF, XGBoost, feature engineering), and regime-based modelling
Identify and manage modelling risks including lookahead bias, data leakage, overfitting, slippage, and market-impact effects
Design, test, and oversee multi-system, multi-regime strategies for real portfolio implementation
Work directly with the CIO on strategy refinement, validation, and portfolio construction
Produce and communicate regular strategy updates across digital channels (email, dashboards, WhatsApp Business, etc.)
Manage digital distribution lists, product access links, and ensure smooth communication with internal/external stakeholders
Partner with Sales, Marketing, and Compliance to ensure materials meet brand, regulatory, and client standards
Requirements:
Strong academic background in mathematics, statistics, quantitative finance, engineering, computer science, or related fields
Demonstrated ability to take ownership of live portfolios and deploy systematic strategies quickly
Minimum 2+ years of experience in quantitative research or systematic portfolio management within asset management, hedge funds, or proprietary trading
Advanced Python engineering skills with experience building modular, maintainable research and production systems (NumPy, pandas, scikit-learn, XGBoost, statsmodels, plotting libraries, MLOps utilities)
Strong knowledge of backtesting frameworks, walk-forward and rolling-window testing, and model-validation best practices
Practical experience managing model bias, operational risk, and research-to-production transitions
Equities experience preferred
strong candidates from macro, FX, fixed income, or commodities backgrounds will also be considered