Senior Data Scientist – Commodity Markets

Responsibilities

• Maintain and enhance applications in Python (Dash) to put the “human in the loop” with Data Science models
• Drive and support R&D and operation of data-driven analytical market models: Together with our analysts and market experts, you will co-develop strong quantitative market models to predict key market variables drawing from a wide variety of techniques; guide and support others in the development of models, implement new models as well as model enhancements, such as driving cloud-based model and process automation
• Engage with internal market experts to explain our models and insights and to discuss the market structure and key drivers - translating domain knowledge back into our models and building a deep understanding of operations and decision making in our markets
• Drive maintenance and improvements of the code base and code structure for operational models and backend data architectures
• Develop, test, and roll out new application functionality and change requests following product owner prioritization
• Manage operational models & applications (monitoring, refactoring, update of live services)
• Review and debug code and ensure good code documentation
• Enable efficient communication across teams through fast prototyping and demos of potential solutions
• Expand your knowledge, by learning new techniques and applying them to existing and new analytical problems in our markets. Actively share knowledge and innovation areas with your peers

Requirements

• Minimum of 3 to 5 years of practical experience as a software engineer, preferably in the context of markets and market prices
• Excellent Python skills and experience with Python packages for applications (such as Dash)
• Professional use of version control tools in projects involving several developers
• Experience with designing and managing containerized applications and familiarity with cloud platforms (e.g. AWS)
• Data handling and data modeling knowledge and experience using relational databases (e.g. PostgreSQL)
• Holistic perspective on business value from analytics and a strong ability to explain complex topics to diverse stakeholders at the appropriate level, strong communication and engagement
• Preferably previous exposure to Data Analytics or Data Science and an interest in working on analytics and forecasting projects heavily reliant on human / machine collaboration