As a Data Analytics Intern — Trading, you'll work on real business requirements alongside analysts, quants, and data engineers. Your job is to help us get more from our data — extracting and cleaning it, automating manual work, and building the tools and analytics that trading teams rely on.
This is a hands-on, technical role. You'll be expected to code — from data cleaning in Python or SQL through to automation, analysis, and the use of AI. We'll match the depth of your work to your skills and to what the desk needs.
This role sits within the analytics function supporting the trading desks, turning data into insights that inform market decisions — with exceptional candidates having the opportunity to work increasingly closely with the desks over time.
Advanced scope (quant & engineering): If you have serious quantitative and coding depth, you can go further — building statistical/ML models and backtesting research on large datasets that feed directly into trading workflows.
KEY RESPONSIBILITES
- Extract and clean data from APIs, web sources, portals, and internal databases
- Write Python/SQL to build the pipelines, scrapers, and tools the desk needs
- Apply LLMs and AI tools to problems that used to take manual effort
- Prepare charts, dashboards, and summaries for the desk
- Track vessel movements, cargo flows, refinery events, and supply/demand indicators
- Investigate and flag data-quality issues
- Advanced Scope (Quant & Engineering)
- 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
- Optimise and refactor existing systems for performance and reliability
EDUCATION AND EXPERIENCE REQUIREMENT
- Pursuing a degree in a STEM, quantitative, analytical, or business field — for example Engineering, Computer Science, Data Science, Mathematics, Statistics, Quantitative Finance, Economics, Finance, Business, or Business Analytics
- An ability to code in Python and/or SQL — from data cleaning upward — or clear evidence that you can learn fast
- Strong analytical, problem-solving, and attention-to-detail skills
- The ability to take a business requirement and deliver a working result
- An interest in commodities, shipping, or data-driven analytics
NICE TO HAVE
- Experience with data analysis, web scraping, or light automation
- Hands-on with Python data libraries (e.g. pandas, NumPy)
- Familiarity with statistics or machine learning (e.g. scikit-learn, PyTorch, TensorFlow)
- Exposure to cloud/data platforms (Databricks, AWS, Azure)
- BI tools (Power BI, Tableau)
- Understanding of commodity fundamentals or shipping logistics
Who we are
Glencore is one of the world’s largest global diversified natural resource companies and a major producer and marketer of more than 60 commodities. Through a network of assets, customers and suppliers that spans the globe, we produce, process, recycle, source, market and distribute the commodities that advance everyday life.
With over 140,000 employees and contractors and a strong footprint in over 30 countries in both established and emerging regions for natural resources, our marketing and industrial activities are supported by a global network of offices.
Glencore’s customers are principally 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.
Important information
This material does not purport to contain all of the information you may wish to consider. For further important information, including in connection with forward-looking statements and other cautionary information, refer to the Important notice section of Glencore’s 2025 Annual Report, which is available at glencore.com/publications. By their nature, forward-looking statements involve known and unknown risks, uncertainties and other factors which may cause actual results, performance or achievements to differ materially from any future events, results, performance, achievements or other outcomes expressed or implied by such forward-looking statements. This document does not constitute or form part of any offer or invitation to sell or issue, or any solicitation of any offer to purchase or subscribe for any securities.
Other information
The companies in which Glencore plc directly and indirectly has an interest are separate and distinct legal entities. In this document, “Glencore”, “Glencore group” and “Group” are used for convenience only where references are made to Glencore plc and its subsidiaries in general. These collective expressions are used for ease of reference only and do not imply any other relationship between the companies. Likewise, the words “we”, “us” and “our” are also used to refer collectively to members of the Group or to those who work for them. These expressions are also used where no useful purpose is served by identifying the particular company or companies.
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