Data Analytics Intern — Trading

As a Data Analytics Intern — Trading, you will work on real business requirements alongside analysts, quants, and data engineers. Your role is to help us maximize the value from our data—extracting, cleaning it, automating manual tasks, and building the tools and analytics that trading teams rely on.

This is a hands-on, technical role that requires coding abilities, spanning from data cleaning in Python or SQL through to automation, analysis, and the application of AI. The scope of your work will match your skills and the needs of the desk. This role supports the analytics function for trading desks, translating data into insights that inform market decisions; exceptional candidates will have the opportunity to work increasingly closely with the trading desks over time.

Key Responsibilities

• Extract and clean data from APIs, web sources, portals, and internal databases.
• Write Python and SQL to build the pipelines, scrapers, and tools required by the desk.
• Apply LLMs and AI tools to solve problems that previously required manual effort.
• Prepare charts, dashboards, and data summaries for the trading desk.
• Track vessel movements, cargo flows, refinery events, and supply/demand indicators.
• Investigate and flag data-quality issues.

Advanced Scope (Quant & Engineering)

If you possess strong quantitative and coding depth, you can take on advanced projects, including:

• Develop statistical and machine-learning models for trading workflows.
• Run quantitative research and backtesting on large datasets.
• Build production-grade Python and automated data pipelines.
• Optimize and refactor existing systems for enhanced performance and reliability.

Education and Experience Requirements

• Currently pursuing a degree in a STEM, quantitative, analytical, or business field (e.g., Engineering, Computer Science, Data Science, Mathematics, Statistics, Quantitative Finance, Economics, Finance, Business, or Business Analytics).
• Proficiency in coding in Python and/or SQL, from data cleaning upward, or clear evidence of rapid learning ability.
• Strong analytical, problem-solving, and attention-to-detail skills.
• The ability to translate a business requirement into a functional, deliverable result.
• A genuine interest in commodities, shipping, or data-driven analytics.

Nice to Have

• Experience with data analysis, web scraping, or light automation.
• Hands-on experience with Python data libraries (e.g., pandas, NumPy).
• Familiarity with statistics or machine learning frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
• Exposure to cloud or data platforms (Databricks, AWS, Azure).
• Experience with BI tools (Power BI, Tableau).
• Understanding of commodity fundamentals or shipping logistics.

About Glencore

Glencore is one of the world’s largest global diversified natural resource companies and a major producer and marketer of over 60 commodities. Through a global network of assets, customers, and suppliers, we produce, process, recycle, source, market, and distribute the commodities that advance everyday life.

With over 140,000 employees and contractors and a strong operational footprint in over 30 countries across established and emerging natural resource regions, our marketing and industrial activities are supported by a global network of offices.

Glencore’s customers include major industrial consumers, such as those in the automotive, steel, power generation, battery manufacturing, and oil sectors. We also provide financing, logistics, and other services to producers and consumers of commodities.