Quantitative Developer-Systematic Trading Commodities

Overview

Quantitative Developer - Systematic Trading Commodities

BGN is seeking a Quantitative Developer to join our team on the ground floor of an ambitious build. We are developing a systematic trading platform for global commodity markets, supported by an investment process rooted in statistical pattern detection and quantitative risk management.

This is a unique opportunity to work directly with the Senior PM and Head of Paper and Systematic Trading, owning the infrastructure and quantitative methods that take research ideas to production in a fast-moving, real capital environment.

Responsibilities

• Experience migrating systems from prototype to production
• Build and maintain data pipelines ingesting futures price data from Bloomberg, vendor feeds, and Timescale DB
• Design and build a walk-forward back testing engine for seasonal spread strategies with realistic slippage, transaction costs, and execution assumptions
• Validate detection methods for look-ahead bias, overfitting, and out-of-sample robustness
• Audit and improve the statistical detection pipeline (hypothesis testing, multiple comparison corrections, composite scoring, regime-conditional analysis)
• Build portfolio construction logic, position sizing, and risk management (VaR, expected shortfall, portfolio-level constraints)
• Develop performance dashboards and alerting for data quality, model drift, and portfolio P&L tracking
• Migrate and scale data infrastructure (SQLite to PostgreSQL), implement caching, and optimize detection runtime
• Productionize signals into the live trading stack with CI/CD, automated testing, monitoring, and version control
• Collaborate directly with the Senior PM - Head of Paper and Systematic Trading to translate trading hypotheses into robust, testable experiments and improve the approach computationally

Requirements

• 5-10 years of experience in quantitative development at a prop trading firm, trading house, hedge fund, or similar
• Fluency in Python and SQL; clean, testable, production-grade code is a must
• Deep understanding of commodity futures: spreads, rolls, seasonality, term structure, contract expiry mechanics
• Statistical fluency: hypothesis testing, resampling methods, multiple comparison corrections, time-series analysis
• Experience building or contributing to back testing systems with realistic execution assumptions
• Familiarity with relational databases (PostgreSQL preferred) and query optimization
• Self-starter with the ability to work autonomously in a lean, high-ownership environment
• Bachelors in CS, Computer Engineering, Mathematics, Statistics, or computationally heavy subject matter