Quant Researcher, Commodities

directly with portfolio managers, traders, and fundamental analysts to generate differentiated market insights and actionable trade ideas.

• Contribute to the research architecture and analytics platform used by the investment team. Qualifications & Requirements Education PhD, MSc, or BSc in a highly quantitative discipline such as mathematics, physics, computer science, engineering, statistics, economics, or operations research. Technical Skills Strong C++ skills, with experience building high-performance analytical libraries and models. Strong Python skills for research, prototyping, data analysis, and workflow orchestration. At least 5 years of experience in a quantitative research, commodities, logistics, or applied modeling environment. Experience designing and integrating models into front office risk platforms and pricing tools. Quantitative / Research Skills Strong grounding in probability, statistics, linear algebra, optimization, and numerical methods.
Experience with quantitative approaches such as:
• time series analysis forecasting simulation optimization under constraints network and flow models stochastic modeling Ability to formulate ambiguous market questions into testable hypotheses and practical research outputs. Market / Domain Knowledge Experience in, or strong interest in learning about, physical commodity markets, with emphasis on energy markets. Familiarity with areas such as: inventory and storage economics freight markets and transport constraints pipeline, terminal, warehouse, vessel, rail, and truck logistics regional dislocations and infrastructure bottlenecks physical optionality embedded in commodity systems Exposure to oil, gas, power, refined products, metals, or agricultural commodities is a plus. Preferred Experience Prior experience in a front-office quantitative, trading, or investment-oriented roles. Experience building models used directly in risk
platforms, trade evaluation, or idea generation.
• Familiarity with SQL/NoSQL databases, distributed data workflows, or DAG-based systems. Knowledge of machine learning techniques where relevant to forecasting or classification is a plus. Personal Attributes Strong commercial instincts and interest in applying quantitative research to investment decisions. Ability to work independently and as part of a team. Intellectually curious, highly analytical, and motivated by complex market problems. Comfortable working directly with investors and operating in a fast-paced front-office environment.", "salary_raw": "Row(double=None, string=None)"}