Systematic Portfolio Manager – Goodman Masson

Institute of Project Management; Greater London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A growing investment management firm is seeking a talented Systematic Portfolio Manager willing to relocate to Oman. In this high-impact role, you will take responsibility for managing systematic portfolios, leading research and development, and working directly with senior management. Ideal candidates have strong academic backgrounds in quantitative fields, a minimum of 2+ years of relevant experience, and excellent Python programming skills. The firm offers the opportunity to thrive in a dynamic and entrepreneurial environment.

Qualifications

• Minimum 2+ years of experience in quantitative research or systematic portfolio management.
• Experience stepping into portfolio responsibility on tight timelines.
• Strong knowledge of backtesting frameworks and model-validation best practices.

Responsibilities

• Take over and systematize multiple existing sub-portfolios within 3 months.
• Lead full-cycle development of quantimental investment systems.
• Design, test, and oversee multi-system strategies for real portfolio implementation.

Skills

Advanced Python engineering skills

Quantitative finance

Statistical analysis

Machine Learning

Education

Strong academic background in mathematics or related field

Tools

NumPy

pandas

scikit-learn

XGBoost

Job description

• Location London, England, United Kingdom

Systematic Portfolio Manager - Relocation to Oman

Employer: Growing Investment Management Firm

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.

Role Overview

As a Systematic Portfolio Manager, you will take immediate responsibility for several live sub-portfolios, transitioning them to fully systematic oversight within the first three months. You will lead research, development, deployment, and ongoing management of data-driven investment systems, working closely with the CIO and broader investment team.

Key Responsibilities

• 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.

Qualifications & Experience

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-ideally with experience stepping into portfolio responsibility on tight timelines.

Minimum 2+ years of experience in quantitative research or systematic portfolio management within asset management, hedge funds, or proprietary trading.

Technical Skills

• 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.