Intern – Data Scientist (AI Focus)– Commodity trading desk

Commerzbank Corporates clients (CC) is the integrated Corporate and Investment Banking division of Commerzbank AG. CC provides corporate and institutional clients with a broad range of commercial and investment banking products. It is active in Europe’s most important markets with main offices in Frankfurt and London. The business incorporates capital markets activities in commodities, interest rates and foreign exchange with a strong focus on derivatives. It provides clients with capital and debt raising solutions and offers its customers access to a broad range of investment and risk management products across all major asset classes.

The Commodity trading team within Financial Markets (FM) is a product specialist team with the responsibility of market making and risk managing the whole Commodity products suite of the bank. This includes Precious metals, Base metals, Oil & Gas, Power, Carbon and Agricultural products. Our clients are corporates who manage their commodity price risk; and their banks that act as their counterparties when Commerzbank has no relationship with the end-client. We provide tailor-made Commodity hedging solutions to our clients across the globe. We design complex Commodity trading strategies to maximize the value of our client franchise and optimize the P&L for the bank.

Key activities:

• Tool Development and Automation: Design and develop Python-based tools and AI-driven prototypes to automate and streamline front office workflows. Build and maintain GUIs/dashboards that display continuously updated quotes and market information. Apply and promote Commerzbank Python code design rules and good software engineering practices, ensuring clean structure, readability, and maintainability in code written for and with colleagues on the desk.
• AI Integration and Optimization: Demonstrate the use of agentic AI capabilities to support and optimize code development for team members, including using AI to refactor, document, and debug code. Implement and share optimization techniques for code performance and development workflows, helping colleagues improve the efficiency and robustness of their tools.
• Collaboration and Business Alignment: Work closely with traders, quants, and other front office staff to identify relevant data sources, clarify requirements, and iterate on prototypes to ensure tools are aligned with real business needs. Document solutions, provide structured handovers, and support users in testing and integrating tools into their daily processes.
• AI Adoption and Training: Help the Commodities desk onboard and use AI tools available within the bank, including explaining available AI services and practical use cases, providing guidance on prompt design and best practices, and highlighting limitations and control mechanisms.
• Organizational and Communication: Gather requirements from traders and other stakeholders and translate them into clear technical specifications. Communicate complex technical topics in an accessible manner to non-technical stakeholders. Onboard and support the various Commodity desks in the practical use of AI tools, including training sessions and demonstrations.

Key requirements:

• Graduated in the last 12 months or graduating in 2026. The successful candidate is likely to be educated to degree level in Data Science, Computer Science, Statistics, Mathematics, Engineering, Physics, or Quantitative Finance.
• Technical Skills: Excellent numerical and analytical skills. Solid problem-solving abilities. Strong Python programming skills, including data processing, automation, API integration, and basic GUI/dashboard development. Practical experience with Large Language Models (LLMs) and LLM-based services, including prompt design and iterative refinement, evaluation and comparison of model outputs, and integration of LLMs into data pipelines and tools. Ability to build software prototypes/Proofs of Concept (PoCs) and develop them into robust, stable tools. Familiarity with real-time or near real-time data processing from unstructured sources such as chat streams.
• Market Knowledge: Basic understanding of financial markets and commodities products sufficient to understand pricing information, quote structures, and trading workflows. Business knowledge from a previous internship within Financial Services is a plus. Solid understanding of derivatives products at a theoretical and practical level.
• Software Engineering Practices: Experience with coding standards and version control; adherence to internal Python design principles.
• Analytical and Problem-Solving Skills: Ability to analyze loosely defined business problems and translate them into clear technical requirements. Structured approach to experimentation (e.g., prompt tuning, model selection, error analysis). Capability to design workflows that combine automated processing with appropriate human oversight to ensure data quality and control.
• Organizational and Communication Skills: Strong verbal and written communication skills. Team player. Ability to communicate complex technical topics in an accessible manner to non-technical stakeholders. Coordination with multiple desks and prioritization of tasks under time constraints.