Data Scientist (Oil Trading)

A global energy trading organisation is hiring a Data Scientist to support market analytics across commodity trading. The role uses data science, quantitative analysis and Python to build models, tools and insights that help analysts and traders understand supply, demand, pricing, flows and market risk.

Responsibilities

• Build models and analytical tools to support supply-demand analysis, market forecasting and scenario analysis.
• Apply machine learning, statistics and time-series methods to market, pricing, freight, weather and flow data.
• Extract practical insights from large and complex datasets to support commercial and trading decisions.
• Develop dashboards, APIs or other tools that help teams access and use data more effectively.
• Validate models so outputs are transparent, reproducible and suitable for a trading environment.
• Work with analysts and commercial stakeholders to improve data quality, assumptions and model inputs.
• Explain technical findings clearly and translate them into practical recommendations.
• Document tools and support adoption so analytics can be used in daily workflows.

Skills & Experience

• Degree in Data Science, Mathematics, Statistics, Computer Science, Engineering, Physics, Economics or another quantitative discipline.
• At least 5 years of experience in data science, advanced analytics or quantitative modelling.
• Strong Python skills, including libraries such as Pandas, NumPy, SciPy and scikit-learn.
• Experience with machine learning, time-series forecasting, anomaly detection, optimisation and statistical modelling.
• Experience working with large, messy datasets and building models for forecasting or decision support.
• Ability to communicate clearly with non-technical stakeholders.
• Exposure to commodity, energy, financial markets or trading environments.
• Experience building dashboards, APIs, analytical applications or decision-support tools.
• Familiarity with version control, testing, reproducible workflows, CI/CD or Agile delivery.
• Cloud experience, such as AWS, Azure, containerisation or serverless analytics.

Contact

Dawn Gulanes

EA Personnel no. R1551518

EA Licence no. 18S9419