3+ years of professional Python experience for data analysis
Strong background in commodities markets, especially crude oil
Proficiency in Git/version control systems
Applied statistics experience including regression and time-series
Understanding of machine learning fundamentals and tools
Familiarity with SQL and relational databases like Snowflake
Experience in data visualization, preferably with Streamlit
Knowledge of cloud platforms like AWS or Azure
Ability to handle large, noisy datasets
Conceptual understanding of NLP and neural networks
Focus on building impactful research tools.
Responsibilities
Develop and maintain Python-based research and data pipelines
Design and manage structured data workflows and databases
Build dashboards and interactive visualization tools for trading
Apply statistical techniques and machine learning to market data analysis
Refine quantitative signals and strategies for commodities trading
Implement practical AI applications relevant to trading
Collaborate closely with traders to translate intuition into quantitative methods
Benefits
Collaborative work environment with a world-class team
Opportunity to influence trading strategies and P&L directly
Access to cutting-edge technology in quantitative finance
Involvement in multi-strategy investment methodologies
Potential for professional growth in a dynamic hedge fund context
Full Job Description
Role Overview
We are seeking a quantitative researcher to join a world class commodities trading team. This role is focused on building and enhancing data, analytics, and quantitative tools that support discretionary trading decisions. The ideal candidate combines strong coding and statistical foundations with practical experience applying machine learning and early-stage AI techniques to real-world problems. Prior exposure to crude oil markets is strongly preferred.
Responsibilities
Develop and maintain Python-based research, analytics, and data pipelines to support trading and market analysis.
Design and manage databases and structured data workflows, including SQL-based querying and cloud-hosted data solutions.
Build dashboards and interactive tools (e.g., Streamlit) to visualize market data, signals, and risk metrics for the trading desk.
Apply statistical techniques and machine learning methods to analyze historical and real-time market data.
Contribute to the development and refinement of quantitative signals and core strategies used in commodities trading.
Explore and implement practical AI applications, including NLP and neural network-based approaches, where relevant to the trading process.
Work closely with the trader to prioritize projects, translate trading intuition into quantitative frameworks, and iterate quickly.
Qualifications
Python (minimum 3+ years of professional experience; required) for data analysis.
Prior experience in commodities markets, particularly oil, is strongly preferred.
Git / version control (required).
Solid applied statistics (e.g., linear and logistic regression, autocorrelation, time-series concepts).
Machine learning fundamentals and common tools (e.g., SVMs, model evaluation best practices).
SQL and relational databases (e.g., Snowflake).
Dashboarding and data visualization (ideally Streamlit).
Cloud platforms (AWS, Azure, or similar).
Experience working with large, noisy, real-world datasets.
Strong conceptual understanding of NLP and neural networks.
Motivated to build tools and research that directly impact P&L.
Excellent written and verbal communication skills.
High level of intellectual curiosity, strong work ethic, and a keen attention to detail.
Ability to work effectively in a team-oriented, fast-paced, and dynamic environment.
