Quantitative Developer-Systematic Trading Commodities

OverviewQuantitative Developer - Systematic Trading CommoditiesBGN 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.ResponsibilitiesExperience migrating systems from prototype to productionBuild and maintain data pipelines ingesting futures price data from Bloomberg, vendor feeds, and Timescale DBDesign and build a walk-forward back testing engine for seasonal spread strategies with realistic slippage, transaction costs, and execution assumptionsValidate detection methods for look-ahead bias, overfitting, and out-of-sample robustnessAudit 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 trackingMigrate and scale data infrastructure (SQLite to PostgreSQL), implement caching, and optimize detection runtimeProductionize signals into the live trading stack with CI/CD, automated testing, monitoring, and version controlCollaborate directly with the Senior PM - Head of Paper and Systematic Trading to translate trading hypotheses into robust, testable experiments and improve the approach computationallyRequirements5-10 years of experience in quantitative development at a prop trading firm, trading house, hedge fund, or similarFluency in Python and SQL; clean, testable, production-grade code is a mustDeep understanding of commodity futures: spreads, rolls, seasonality, term structure, contract expiry mechanicsStatistical fluency: hypothesis testing, resampling methods, multiple comparison corrections, time-series analysisExperience building or contributing to back testing systems with realistic execution assumptionsFamiliarity with relational databases (PostgreSQL preferred) and query optimizationSelf-starter with the ability to work autonomously in a lean, high-ownership environmentBachelors in CS, Computer Engineering, Mathematics, Statistics, or computationally heavy subject matter#J-18808-Ljbffr