About the role
AI summarisedJoin a high-performing Front Office Point of View Trading Quant team focused on generating, testing, and delivering robust trading signals and indicators for the Crude and Refined Products markets. You will apply statistical modeling, machine learning, quantitative research, and market microstructure intuition to uncover predictive patterns and convert them into actionable insights for traders.
UtilitiesOnsiteTrading
Key Responsibilities
- Generate and research trading strategies for crude and products markets using statistical methods, machine learning, feature engineering, and quantitative modeling.
- Collaborate with traders to translate market intuition into testable hypotheses, validate signal behavior, and improve decision-making across short and medium term horizons.
- Analyze large volumes of market, fundamental, and alternative data to identify patterns, anomalies, and structural behaviors relevant to Crude & Products trading.
- Build and maintain robust backtesting frameworks, evaluate strategy performance, stress test signals, and ensure statistical validity across market regimes.
- Develop scalable research tooling and systematic strategy components using Python and modern data science libraries.
- Contribute modeling insights to broader systematic and data-driven initiatives across the trading organization.
- Monitor live strategy behavior, support execution logic improvements, and partner with developers to deploy production-ready analytics.
Requirements
- Strong quantitative background (MSc/PhD preferred) in applied math, statistics, econometrics, data science, computer science, or similar fields.
- Expertise in statistical modeling, machine learning, predictive analytics, feature engineering, and time series methods applied to financial or commodity markets.
- Advanced Python skills for research, modeling, and data processing.
- Ability to analyze large datasets, uncover signal patterns, and communicate findings clearly to traders and commercial teams.
- Experience building and validating backtests, including performance attribution, robustness checks, and sensitivity analysis.
- Curiosity about market structure, pattern discovery, and quantitative alpha generation in commodity futures and spreads.
- Proactive, research-driven mindset with strong documentation habits and attention to statistical integrity.